Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ENHANCED SPECIALIZED METABOLITE, TRICHOME DENSITY, AND BIOSYNTHETIC GENE EXPRESSION IN STEVIA REBAUDIANA (BERTONI) BERTONI PLANTS INOCULATED WITH ENDOPHYTIC BACTERIA ENTEROBACTER HORMAECHEI
Review round: 1
Reviewer: 1

Output:
Basic reporting: The present manuscript reports the effects of inoculation with endophytic bacterial isolates on the growth and specialized metabolism of Stevia rebaudiana (Bertoni) Bertoni. I think that the Authors studied an important topic using adequate methods. The structure of the presented manuscript is typical for research article. Background is well described, methods are clear (with some exceptions) and data is presented concisely. My major comments pertain to the lines 140-141, 145-146 and 169-170 (methods). These issues need detailed explanation. Aim of the study is clear but 2-3 hypotheses must be stated. Some fragments in Discussion section must be reworked (as they can be more in-depth). Additionally, the Authors can consider to conduct more statistical analyses (this is not mandatory in my opinion and I feel that this is for the decision of the Editor). The language needs slight polishing but article is readable. The raw data is shared (but need clarification as mentioned above), and figures are of good quality. I believe that this article can be published after revision.
Experimental design: The study falls within the scope of PeerJ. The aim is clear, however, the hypotheses must be stated (see detailed list of comments). Methods are clear with some exceptions (see list of comments).
Validity of the findings: In my opinion the findings are validate, however, they can be improved as there were some purely descriptive fragments in the Discussion. In such case, some ideas for the Authors were proposed. The authors can try to do some more statistical analyses in order to see wider image of this study (e.g., correlation of the traits, differences between inoculants), but this is optional, not mandatory.
Additional comments: Request for global changes and improvements:
The term “secondary metabolites” was used for a very long time, but the term “specialized metabolites” has been used more often recently, as it is not known if these compounds are of secondary importance. I believe that this change is not mandatory but very desirable; you can consider such change.

Please always use full species name when introducing them for first time, e.g., Lycoris radiata (L'Hér.) Herb. but not Lycoris radiata. Please check it and change where applicable for other species. You can use the Plant List database or World Flora Online database.

Species naming needs more uniformity. When you introduce name of species, e.g., Stevia rebaudiana (Bertoni) Bertoni, give the full name and, then, use consequently stevia (common name) or S. rebaudiana (short species name) but do not mix these conventions throughout the manuscript (as in lines 61 and 62). Both forms are fully acceptable, however, please select one of them. Additional comment: APG IV classification states that the correct name of the studied species is Stevia rebaudiana (Bertoni) Bertoni. Thus, please use this name or cite reference for Stevia rebaudiana Bertoni if there is a rationale.

I believe that names of proteins (and nonprotein metabolites as well), including enzymes should be presented without italics, while names of their genes should be italicized. Please check it carefully throughout the manuscript, from line 65. For example, it is done very well in line 90, but in L66-73 needs some attention.

As this work describes biochemical analyses, I believe that SI units should be used, e.g., cm3 but not mL and dm3 but not L, etc. Additionally, please replace mg/g in the dataset for mg g-1 as you did in the figures. Please check and change where applicable.

Were the leaves that you used for quantification of metabolites fully developed? Were they from upper or lower part of the plant? Please provide this information when you describe procedure for extraction.
Please provide EC numbers of enzymes that you describe and study.

Request on statistical analysis and data collection: I wonder that for some measurements you consider only control, H2A3-, H5A2- and R7A7-inoculated plants because control is always needed, H2A3-, H5A2-inoculated plants showed the best induction of steviol glycosides biosynthesis but why R7A7-inoculated plants? Was there any rationale? If was this the group with the lowest performance, that should be clearly showed in Figure 1 and Figure 2. Additionally, more information can be extracted from this dataset. Dunnett’s post-hoc test was used to compare treated variants with control variant (it is the correct approach), but comparison among all the studied variants is also very valuable. Were there any differences between H2A3-, H5A2-inoculated plants? How about other treatments? This can be analyzed using other post hoc tests which I strongly recommend. Then, the selection of the R7A7 variant is fully justified. Was there any other rationale? This change is not mandatory but 1) there is chance to introduce statistical error due to approach you used, and 2) your findings can be misinterpreted by the audience. I understand that conducting statistical analysis from scratch is time-consuming, but in my opinion this is very desired for this dataset. Additionally, did you consider correlation analysis? Maybe there is correlation between number of trichomes and contents of the studied metabolites (at least in some cases)?

Figure 1: Stevioside and rebaudioside A have the same color-based annotations. The use of color artwork in the PeerJ is free of change; thus, you can use color to correct this issue.

Figure 3: Size should be greater (see detailed list of comments).

Detailed list of issues:
L41: You can consider to introduce the information that plant-bacteria associations can be phyllospheric, rhizospheric and endophytic.
L52: I think that capital letter should there used, as name of taxon is presented: Amaryllidaceae alkaloid (like in eponyms).
L55: Please remove italics from “Bertoni”.
L56: I believe that it can be stated that Stevia rebaudiana (…) is an economically important crop due to its ability to accumulate specialized metabolites called steviol glycosides (SG), including isosteviol, stevioside, rebaudiosides (A, B, C, D, E and F), steviolbioside and dulcoside A that are used as low-calorie sweeteners (…).
L59: Capital letter should not be used for stevia common name in the middle of the sentence (please check where applicable).
L77-79: These two sentences can be rewritten to improve their structure, e.g.: In S. rebaudiana, the presence of both short and long glandular trichomes has been reported (Sarmiento-López et al., 2021). The following fragment must be corrected: “they increase in leaves by arbuscular mycorrhizal colonization, as well as the accumulation of phenolic compounds”. Does it pertain to their number or size? Additionally, their size/number increase due to enhanced specialized metabolism, or content of specialized metabolites increase due to mycorrhizis?
L88: The meaning of AM should be introduced.
L95: The aim of the study is very well presented, but this is the best place to state your hypotheses. Do you expect improved biosynthesis of the studied metabolites as the result of inoculation? Can it be hypothesized a priori that the studied bacterial isolates will trigger different (or the same response)? Will there be any trade-offs between plant growth per se and biosynthesis or both growth and biosynthesis be improved? Please present here 2-3 short hypotheses that piqued your curiosity about this topic.
L100-105: You refer to the previous work where cultivation of plants was described in detail, but please add detailed information about growth conditions here as well. It will improve the readability. Additionally, were these cultures centrifuged+washed before incolutaion of plants?
L107: Please describe in detail procedure of isolation of endophytic bacteria, e.g., source of donor plants, media used for isolation, etc. Did you use all isolated strains or were there any additional criteria for their selection?
L115: Were the plants sown or planted?
L124: Height measurements: was initial height of the plants subtracted from their final height? Were they planted the same way to avoid subtraction?
L126: Please correct, I propose: (…) to determine their dry weight
L136: If possible, please provide also g value for centrifugation
L140-141: You used six plants per treatments and two replications but in the dataset I can see only three values per treatment. Were the samples pooled? It is often used method to reduce the costs or time, and in my opinion is acceptable, but it must be described in detail.
L145-146: It must be clarified. Did you mean that 0.1 g of sample was extracted in 100 cm3 of ethanol when you stated 0.1%? You can also state that the sample to fluid ratio was 1:1000.
L152: Please provide concentration range of the calculated standard curve and R^2 value (proof of linearity).
L158: Please provide concentration range of the calculated standard curve and R^2 value (proof of linearity).
L164: Were the leaves you used fully developed?
L169-170: You stated that counting of trichomes was performed according to the Sarmiento-López et al. (2021). However, this work does not contain details on differentiation between long and short trichomes. I tried to identify them on Figure 3 and this is easy and possible, however, I recommend to organize this figure in new manner. Instead of 1x5 figure (where all photographs are small) use 3+2 or 2+2+1 to make these image larger. The audience must see that short trichomes were short and long were long. Were they counted on whole area of 0.2826 cm2 (as in the Sarmiento-López et al., 2021)? It must be stated in detail.
L207-208: Needs language correction.
L212-288: Please always use term “significantly” when you state the differences, e.g., significantly lower, significantly higher, significantly different.
L212-288: If the difference were significant, always state their magnitude, e.g., 1.65 times more, 65% more, 165% of the control variant, etc.
L283-288: This needs attention, as in the Results section you should not reference to the previous studies.
L291: Plant–endophytic
L293-294: Did you mean screening/isolation of endophytic bacterial isolates and their reinoculation? Please rewrite this sentence.
L294-295: You can also state that inoculation did not halt nor reduce plant growth. It is common that plants must divide their resourced into growth of comping with stimuli. In my opinion, it is very important information that yield quality was improved whilts its quantity was not reduced. It can also be another sign that the studied isolates were endophytic but not random or facultative pathogenic.
L298: in this context, “triggered” is probably better word than “produced”.
L305: Fragaria ananassa but not Fragaria ananassia. Please also verify other plant names.
L313: Please change NPK to “nitrogen, phosphorus and potassium” or “macronutrients (nitrogen, phosphorus and potassium”.
L323-325: This can be stated better: “(…) the accumulation of SG (Kilam et al., 2015; Vafadar, Amooaghaie & Otroshy, 323 2014). However, the present work reports that stimulation of SG biosynthesis can be achieved with selected endophytic bacteria without fungal coinoculation. Although sequentional incoluation with bacteria and fungi does not always yield synergistic effects (Nowogórska and Patykowski, 2015), this issue should be investigated in the future because plants are exposed to numerous environmental stimuli acting simultaneously).” (Nowogórska A, Patykowski J. 2014. Selected reactive oxygen species and antioxidant enzymes in common bean after Pseudomonas syringae pv. phaseolicola and Botrytis cinerea infection. Acta Physiologiae Plantarum 37. DOI: 10.1007/s11738-014-1725-3.
L329-331: You can only state that morphology of trichomes was typical for the studied species.
L333-334: Statement about coincidence of traits should be supported by correlation analysis. Please rewrite this or add additional results pertaining to the analysis of correlation.
L345-361: I feel this fragment too descriptive. I think that you can state that improved contents of the studied metabolites was associated with greater autofluorescence of trichomes and, based on the available data, improved biosynthesis in trichomes is at least partially responsible for the observed effect. Then, it can be discussed in a practical context and through the prism of ecological implications. Is it fully beneficial for plants to contain significantly increased contents of specialized metabolites? How about tradeoffs – plant growth was not halted and biosynthesis was improved, especially in trichomes. Is there any (even hypothetical) explanation for this mode of action?
L371: improved SG accumulation
L381: The expression and biosynthesis is improved and growth is not halted. How about molecular mode of action? Where from these differences come from (i.e., why some strains produce such effect while the others do not)? Please try to find if there are any reported species-specific (or strain-specific) effectors of the studied bacteria that hypothetically contributed to the observed diversity of reactions.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ENHANCED SPECIALIZED METABOLITE, TRICHOME DENSITY, AND BIOSYNTHETIC GENE EXPRESSION IN STEVIA REBAUDIANA (BERTONI) BERTONI PLANTS INOCULATED WITH ENDOPHYTIC BACTERIA ENTEROBACTER HORMAECHEI
Review round: 1
Reviewer: 2

Output:
Basic reporting: Authors have isolated 12 endophytic bacterial strains from different tissues of Stevia and inoculated the plants with these isolates to test the effect of these strains on the plant growth, flavonoid, phenolic and secondary metabolite production. They identified two potential strains which showed higher accumulation of secondary metabolites after inoculation. They also studied the expression of 4 major genes involved in the synthesis of these metabolites.

The authors have provided all the necessary data with sufficient literature background. They have also provided the raw data as supplementary files. The manuscript is well-written but needs a thorough spell check including the table and figure legends. Authors need to include the Table and figure legends with footnotes wherever applicable.
Experimental design: It is understood from the manuscript that authors have isolated the strains from Stevia and re-inoculated the plants with the same strains independently to see the effect of these strains on secondary metabolite production. However, it is not clearly mentioned anywhere in the text so it would be good to describe the methods section with appropriate details.
Methods section should also describe how these 12 different bacterial strains were identified and genotyped.

Additional comments and suggestions are included in the edited version of the manuscript.
Validity of the findings: No comments
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ENHANCED SPECIALIZED METABOLITE, TRICHOME DENSITY, AND BIOSYNTHETIC GENE EXPRESSION IN STEVIA REBAUDIANA (BERTONI) BERTONI PLANTS INOCULATED WITH ENDOPHYTIC BACTERIA ENTEROBACTER HORMAECHEI
Review round: 1
Reviewer: 3

Output:
Basic reporting: In the manuscript, the authors found the endophytic bacteria E. hormaechei H2A3 and E. hormaechei H5A2 increased the SG content and stimulated the density of trichomes in the leaves, as well as the accumulation of secondary metabolites in trichomes. And the accumulation of SG was related to the upregulated of the KO, KHA, UGT74G1 and UGT76G1 genes. There are still many problems with the manuscript. For example, the research objectives are not clear enough, and the results are not discussed in depth. And there are formatting problems in the figure section. So, I suggest that this study be accepted with a major revision. Detailed suggestions are as follows：
INTRODUCTION
1.	In this section, it is hard for readers to find what is the main problem to be solved by the study. The key gaps in the literature need to be identified, the key background information presented to highlight these gaps and the way in which this study then proposes to address them.
2.	The logical connections between paragraphs need to be reconsidered to allow the reader to better grasp the focus of the study.
3.	It would be better to have 2-4 hypotheses here (that arise logically from the introduction). This would also aid the discussion which could then be structured around each hypothesis.
Experimental design: MATERILAS AND METHODS
4.	L107, Were these bacteria isolated in this study? If so, please describe the bacteria isolation method and medium. If not, the corresponding references should be noted. In addition, what is the purpose of this research with bacteria from different parts of plants?
5.	L113, a reference is lacking here.
6.	L114, what is the inoculation method of bacteria? Inoculate in the root? Leaves or soil?
Validity of the findings: RESULTS
7.	It is suggested to distinguish ‘Stevioside’ and ‘Rebaudioside A’ in Figure 1 instead of using the same annotation method
8.	‘Bars represent the mean ± standard deviation of six individual plants harvested to 30 days postinoculation.’ This sentence is repeated twice in the text (Figure 1).
9.	The Figure note is only half written (Figure 3).
10.	‘Control is plants that were not noninoculated.’ This sentence is repeated twice in the text (Figure 4). And the Alphabetic labels are suggested to be enlarged.
11.	The Figure notes of figure5 and 6 also have the problem of sentence repetition. The author should be more carefully in the manuscript written.
12.	The font size of alphabetic labels should be unified, because the letter ‘n’ looks smaller than other letters.
13.	L273, what is the meaning of ‘if’?
14.	L283, a reference is lacking here.
Additional comments: Discussion
15.	The discussion would better be structured around your hypothesis, instead of the detected indicators. The main findings should be pointed out for the reader at first and alerting them to the key points that will be discussed.
16.	The key, novel, findings need to be emphasized for the reader.
17.	L299, I think it's hard to draw a conclusion of ‘growth promotion is not associated with endophytic bacterial’ . This is only the results of the bacteria tested in this study.
18.	L324, the colonization related results were not found in the results section.
19.	L370, a reference is lacking here.
20.	How do the authors explain that H2A3 inoculated plants have higher SG content and density of trichomes, but the expression of SG synthetic genes is lower than that of H5A2?
21.	Authors are advised to discuss their results in depth rather than listing similar studies. Pointing out the research significance, emphasizing innovation, and discussing the future research direction.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ENHANCED SPECIALIZED METABOLITE, TRICHOME DENSITY, AND BIOSYNTHETIC GENE EXPRESSION IN STEVIA REBAUDIANA (BERTONI) BERTONI PLANTS INOCULATED WITH ENDOPHYTIC BACTERIA ENTEROBACTER HORMAECHEI
Review round: 2
Reviewer: 1

Output:
Basic reporting: The Authors introduced great number of corrections. From my point of view, the Authors adressed all questions very well and supported all the data and analyses that I requested.
The manuscript is very well prepared and references fully support the findings. The Authors prepared new set of figures and tables which are of very high quality.
The hypotheses are now clearly stated and the findings match them.
 Overall, the Authors presented outstanding understanding of the topic. I recommed this work as ready for publication with one very minor change that can be introduced during proofing (see comment below).
Experimental design: The hypotheses are clear and concise now. The Authors improved 'Material and Methods' section and supported all the information pertaining to the growth conditions, inoculants and inoculation procedure as well as determination of specialized metabolites. The technical details supported by the Authors strenghted the manuscript in terms of transparence of the methods they used.
Validity of the findings: The Authors introduced new type of statistical anlysis that was requested in my first report. The analyses presented are in-depth and the reader is able to find a full set of information. The conculusions are supported very well by the analyses.
Additional comments: The only change that I request is change of cultivar naming. According to the taxonomical code, name of cultivars should be presented as 'Monaco' and 'Nirmal' but not cv. Monaco or cv. Nirmal. I realize that such nomenclature is used in many works, but it is worth to use correct cultivar naming in such elaborated study.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ENHANCED SPECIALIZED METABOLITE, TRICHOME DENSITY, AND BIOSYNTHETIC GENE EXPRESSION IN STEVIA REBAUDIANA (BERTONI) BERTONI PLANTS INOCULATED WITH ENDOPHYTIC BACTERIA ENTEROBACTER HORMAECHEI
Review round: 2
Reviewer: 2

Output:
Basic reporting: The authors have addressed all the comments and they are satisfactory. There are few minor corrections that need to be incorporated in the manuscript. The suggestions are included to the edited version of the manuscript.
Experimental design: No comments
Validity of the findings: No comments
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: THERAPEUTIC TARGETS AND SIGNALING PATHWAYS OF ACTIVE COMPONENTS OF QILING DECOCTION AGAINST CASTRATION-RESISTANT PROSTATE CANCER BASED ON NETWORK PHARMACOLOGY
Review round: 1
Reviewer: 1

Output:
Basic reporting: The author Cao, et al. is using the systematic network pharmacology through various datasets to identify the active ingredients of QiLing Decoction (QL) and the potential molecular targets of QL for castration-resistant prostate cancer (CRPC). Several interesting targets, such as TP53, VEGFA, IL-6, ESR1, and CASPASE 3, have been identified and might be involved in QL targeting CRPC. In addition, biological experiments showed that QL inhibits human prostate cancer cell PC3-derived tumor growth in vivo.

The bioinformatic studies provided some novelty regarding the QL-active components inhibit CRPC through potential targets/signaling pathways. However, there is a lack of biological experiments to validate the function of identified molecular targets that might be affected by the active ingredients of QL in CRPC.

Note: there are many mistakes of grammars, writings/spellings, and repeats (e.g., line 177-178) in the whole text.  I suggest you have a colleague who is proficient in English and familiar with the subject matter review your manuscript or contact a professional editing service.
Experimental design: 1. Although core targets like TP53, IL-6, VEGFA, Casp3 and ESR1 have been identified, but whether QL active components inhibit tumor growth through targeting one or more of the five top candidates have not been addressed; Similarly, the identified pathway from datasets using computational bioinformatics, but whether and how one of these pathways contribute to QL-mediated CRPC tumor growth are not addressed at all.

The authors showed that in Fig.8. that QL can inhibit PC3-derived CRPC tumor growth in xenograft models. However, what is the core active component from QL? What is the core target for the active ingredients in CRPC? Some targets-based Western and/or RT-PCR could be done to give some answers.

2. Composite ingredients of QI (starting from line 145-148)
The ingredients of the six herb medicines identified from TCMSP database are not from the lists in the 10 individuals of QL (line 77-79). Please explain how these ingredients can be used for QL in your studies.
Validity of the findings: To increase the biological significance of the studies and validate the targets identified from system bioinformatics, one active ingredient from QL and one molecular target in CRPC at least should be verified through experimental studies.
Additional comments: Minor concerns:
1. From line 63 to 76 (Introduction), the authors briefly described how CRPC progress/development and current treatment strategies for CRPC. However, they should give updated and precise background introduction. For example, how androgen receptor (AR) signaling plays an important role in CRPC even in the castration status, FDA approved treatment like PARP inhibitors for CRPC carrying deficiency of for DNA repair, radioligand-based Lu177-PSMA treatment, anti-AR signaling inhibitor Enzalutamide for CRPC, and Taxens-based chemotherapy and more.

2. Please present detailed descriptions in Materials and Methods.

3. In Results section
Figure 2A-D. Labelling words are too small to be recognized, same as in Figure 3 and Figure 5A;


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: THERAPEUTIC TARGETS AND SIGNALING PATHWAYS OF ACTIVE COMPONENTS OF QILING DECOCTION AGAINST CASTRATION-RESISTANT PROSTATE CANCER BASED ON NETWORK PHARMACOLOGY
Review round: 1
Reviewer: 2

Output:
Basic reporting: Language editing is highly recommended, including grammar and sometimes to ensure clarity. I have listed some obvious examples here, but the rest of the text needs a touch by a native speaker too.
Line 70: “there are several reports have been reported as for the chemotherapy for CRPC.” The sentence seemed to have redundance description. I suggested to write as “Several studies have reported chemotherapy for CRPC”.
Line 73: “Abiraterone Acetate has been prove to played … …”, which mixed the past tense and present tense.
Line 77: “....consisting of10 single....”. There is one space missing between “of10”.
Line 82: “....by regulatingglucose-regulated....” also has a space missing.
Line 83: there is an additional “c” in the sentence “....c and codonopsis....”.
Line 90: “psoralea corylifolia could caused the apoptosis”, erroneously used the past tense.
Line 95: it seemed that an additional “t” was inserted.
Experimental design: In general, the research question and the methods are reasonable but more details of the methods needed to be added. The “PCOS-Related Target Network” part, for example, the authors only listed three database and criteria for one database. This is too simple. I suggested to add a brief description to state how to establish the network or cite related reference.

More comments are listed below:
Line 102-103: the authors have used three criteria (OB, DL, and Caco-12 in the manuscript. It is puzzling that you listed “bbb” here but not used it.
Line 107: What is TCMID database? Please give the full name and state the reason that you choose this database.
Validity of the findings: No comment.
Additional comments: Line 150: “51 potential ingredients were screened and were displayed in Table 1”. Based on the three criteria, it is better to give a short description of these 51 ingredients, like active ingredients, potential anti-CRPC effect, etc. The word “screened” was also not properly used here. It looks like you only screened 51 compounds. You screened 1,157 compounds and found 51 potential hits.
Line 147: The term “Suangqi” was not consistent with the legend of figure 2 “hunagqi”.
Line 155-156: the authors mentioned that they established a network of 51 ingredients with their targets. That is too ambiguous. You need to give a description of the figures. At least, you may descript one of them as a sample.
Line 164: “PPI network was established to displayed……”, erroneously used the past tense.
Line 171-172: “Next, depending on……involving ameliorating CRPC”. The sentences need to be refined.
Line 171 and 187: here the authors mentioned 6 core/hub targets were tested. But the PPI network identified 5 hub targets. Is there someone missing?
Line 197: please give a detail description of the figure 7. You do not want the readers to figure out the meaning by themselves.
Line 200: “CRPCxenograft” has a space missing.
Line 226: “……p53-WT could resulted in inhibitory affect……”, erroneously used the past tense.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A COMBINED TEST FOR FEATURE SELECTION ON SPARSE METAPROTEOMICS DATA—AN ALTERNATIVE TO MISSING VALUE IMPUTATION
Review round: 1
Reviewer: 1

Output:
Basic reporting: Overall this article was clearly  written although the organizations were not logical.
References and background are relatively clearly provided.
Experimental design: However, this study had some design issues. For example,
1. The proposed combined test was based on analysis of  technical replicates.
The authors stated that this approach can  guarantee that the true protein abundances are identical. This is not convincing.  Actually in real study, biological variations are more important than technical variations and thus the methods that can detect biological variations are better than the methods based on technical replicates.

2. The combined Fisher combined statistic was  defined as average a Fisher exact test and t-test of their p-values, which needs details on the algorithms.
3. It is true that  feature selection based on imputation is highly dependent on the chosen imputation method. However, how the combined test  addresses the MNAR
and MAR mechanisms  lacked of details. The valuations based on concordance and Pearson correlation are not solid measures. More appropriate measures such as MCC or ROC are required.

4. The  assumption that all variables display the same proportion of missing values is strong.  Actually as the title suggested that metaproteomics data are sparse. Different samples typically have different rates of missing data. The combined method need to design different proportions of missing values to test its robustness.

5. More important, a simulation study is needed.
Validity of the findings: Further works are needed to confirm the findings and the conclusion is not robust.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A COMBINED TEST FOR FEATURE SELECTION ON SPARSE METAPROTEOMICS DATA—AN ALTERNATIVE TO MISSING VALUE IMPUTATION
Review round: 1
Reviewer: 2

Output:
Basic reporting: No comment.
Experimental design: No comment.
Validity of the findings: Handling missing data in proteomics is still an open major issue. Some works use single or multiple imputation to cope with that issue. The authors of this paper propose an alternative methodology for feature selection methods without imputation. The results provided by the authors are supported by various benchmarks and looks promising.
My only concern is about the use of Pearson correlation in several graphs (S3, S6, S7, S8, S11)? Why not use more general correlation coefficient such as Spearman or Kendall since Pearson correlation only detects linear links between variables?
Specific imputation methods were develop for proteomics featuring MEC and POV missing values patterns. For instance, the imp4p methodoogy by Quentin Giai Gianetto
(https://cran.r-project.org/web/packages/imp4p/index.html) tries to addresse them. Yet, it is not cited in the article and it would be interesting if it could be included in the benchmarks. The same suggestion fir the article Goeminne et al. 2020 (https://doi.org/10.1021/acs.analchem.9b04375)
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A COMBINED TEST FOR FEATURE SELECTION ON SPARSE METAPROTEOMICS DATA—AN ALTERNATIVE TO MISSING VALUE IMPUTATION
Review round: 1
Reviewer: 3

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: See attached document for issues and necessary improvements.
Additional comments: See attached document for additional issues.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A COMBINED TEST FOR FEATURE SELECTION ON SPARSE METAPROTEOMICS DATA—AN ALTERNATIVE TO MISSING VALUE IMPUTATION
Review round: 2
Reviewer: 1

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: Congratulations to the authors for having greatly improved the manuscript. The authors took into account some of my suggestions.

Yet, I still think that the use of Pearson Correlations should be replaced by Spearman's or Kendall's ones (Kendall's are generally recommended over Spearman's). Those two types of correlations can capture more general associations the Pearson's ones. They can be used to establish whether two variables may be regarded as statistically dependent. On the contrary, Pearson ones can be used to only establish whether two variables may be regarded as linearly statistically dependent. Moreover, Spearman's or Kendall's correlations can often be used as test statistics in more general settings than Pearson's ones. These tests are non-parametric, as they do not rely on any assumptions on the distributions of X or Y or the distribution of (X,Y). Hence, they may have been used to look for significant associations between the log-transformed p.

I am still wondering why would using Pearson's correlation implies: "Conversely, the order of low p-values has much more importance and this is what is captured by the Pearson correlation via the log-transformation."
On the contrary, it is known that Spearman's or Kendall's correlations capture the association of the variables up to a monotonic transformation and hence will more accurately capture the order of low p-values. With Pearson correlation, you can get a small correlation between variables that are dependent but not linearly (e.g. y=x^2). Why log transforming the p-values would imply getting linear associations between the logged variables? As can be seen on several graphs, it does not seem to be true.

As to the imp4p methodology by Quentin Giai Gianetto, it might have been adapted to zero-inflated data. Yet, I agree with the authors' statement "the imputation is realised separately for each biological condition - which is a way of proceeding that can artificially increases significance of inter-classes differences by enhancing intra-class similarities, and generate a bias in the p-values from subsequent testing procedures."
I am glad that you found the approach by Goemmine very relevant and that you added it to the benchmark.

There is one typo at line 480: UniversitÃ(c) -> Université.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A COMBINED TEST FOR FEATURE SELECTION ON SPARSE METAPROTEOMICS DATA—AN ALTERNATIVE TO MISSING VALUE IMPUTATION
Review round: 2
Reviewer: 2

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: I am believe that the authors have adequately addressed the comments from myself and other reviewers. The revised manuscript reads significantly better than the initial submission, and the authors have done a good job adding relevant details necessary for clarity and reproducibility. The addition of a simulation study has added substantial improvement to the manuscript.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A COMBINED TEST FOR FEATURE SELECTION ON SPARSE METAPROTEOMICS DATA—AN ALTERNATIVE TO MISSING VALUE IMPUTATION
Review round: 3
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: I thank the authors for the additional details on the use of the correlation coefficients that they provide in this revised version of the article.

It leads me to a final comment on the use of a log transform and of Pearson's correlation coefficient.

In the setting of the article, logging data results in two different effects: changing the kind of functional relationship that may be detected using Pearson's correlation as well as weighting differently the domains on which the correlation coefficients are computed. Apparently, the latter effect is of high importance since the authors showed that Pearson's correlation coefficient used after a log transform has better properties than using directly a non parametric correlation coefficient on non logged values.

As a result, it would have been even more meaningful to compare Pearson's correlation coefficient and nonparametric correlation that were computed on the logged data. It would have enabled the authors to detect nonlinear relationships as well as look for significance.
Additional comments: no comment


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A COMBINED TEST FOR FEATURE SELECTION ON SPARSE METAPROTEOMICS DATA—AN ALTERNATIVE TO MISSING VALUE IMPUTATION
Review round: 4
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: I was also suggesting that using Kendall's correlation would have allowed searching for significance.
Nevermind, the article now addresses all my concerns.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: CONTRIBUTION OF HIPPOCAMPAL BDNF/CREB SIGNALING PATHWAY AND GUT MICROBIOTA TO EMOTIONAL BEHAVIOR IMPAIRMENT INDUCED BY CHRONIC UNPREDICTABLE MILD STRESS DURING PREGNANCY IN RATS OFFSPRING
Review round: 1
Reviewer: 1

Output:
Basic reporting: General comments

Under my point of view, the English should be ameliorated and revised by a fluent English professional. The references must be updated, whereas old references must be removed.
The study was well designed and it shows innovative and relevant findings.
Some small changes must be made in order to ameliorate the manuscript, as described at the sequence.

Introduction Section
This section presents repeated sentences, whose focus involves the same issue, but in different words (lines 51-53 and 58-61). In this same section, the subject of the paper must be included, in any time the author explain about the microbiota, for example. In addition, conceptual itens must be included, but not discussion of the findings.

Results
The figure 1A and 1B involve experimental design and therefore  must be separated from both figures 1C and 1D, which show results. In fact, these last figures  must be presented in the Results section.
Experimental design: Experimental design:

The pivotal aim of the study must be clarified.
Authors presented a lot of text about stress, but little about the investigated BDNF/TrkB pathway and microbiota, which together are the differential point of the study.
Validity of the findings: The study has a good impact and is innovative. Underlying data has been provided; are robust, statistically sound and controlled. Conclusions are well-formulated, linked to the original research question, and limited to supporting results.
Additional comments: No more comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: CONTRIBUTION OF HIPPOCAMPAL BDNF/CREB SIGNALING PATHWAY AND GUT MICROBIOTA TO EMOTIONAL BEHAVIOR IMPAIRMENT INDUCED BY CHRONIC UNPREDICTABLE MILD STRESS DURING PREGNANCY IN RATS OFFSPRING
Review round: 1
Reviewer: 2

Output:
Basic reporting: Requires editing by fluent English writer.  I suggest you have a colleague who is proficient in English and familiar with the subject matter review your manuscript, or contact a professional editing service.

The title should be improved. As a suggestion: “Chronic unpredictable mild stress during pregnancy impairs emotional behavior of rats offspring: contribution of hippocampal BDNF/CREB signaling and gut microbiota” or “Contribution of hippocampal BDNF/CREB signaling and gut microbiota to emotional behavior impairment induced by chronic unpredictable mild stress during pregnancy in rats offspring”.

Chronic unpredictable stress (CUMS) is a well-studied model. However, the study novelty should be clarifying. Please added a paragraph in the introduction section highlighting the freshness as well as the importance of the study.
Experimental design: The methodology should be clarified. A didactic form to better comprehension of the protocol is a schematic timeline indicating the days. Here are some examples in the literature (10.1007/s12035-020-02255-z, 10.1007/s12264-021-00754-0).

There are “Gender Differences in the Effects of Prenatal Stress on Brain Development and Behaviour – (s11064-007-9339-4)” and “Sex differences in depression: Insights from clinical and preclinical studies (10.1016/j.pneurobio.2019.01.006)”. In the present study, authors separated the animals offspring in each group of 16 rats (8 male rats,8 female rats). The results are expressed together, male and female mice. Please, discuss about the sex differences in the literature and assume this fragility as a limitation of the study.

The authors studied the effects of CUMS in maternal corticosterone, weight gain, movements and gut microbiota. However, the effects of CUMS in the hippocampus as well as the emotional behavior was accessed only in the rats offspring. If these effects were studied in the mother of the litter too, do the authors think there would be the same emotional behavior and hippocampal signaling of the pups?

The authors used (Li et al., 2020) as methodology for tail suspension test. The aforementioned study used mice rather than rats. In literature is suggested tail suspension test to mice and forced swimming test for mice and rats as methodology for depressive-like phenotype study. In agreement with the reviewer comment: “the forced swim (also termed behavioral despair) test in the rat and mouse, and the tail suspension test in the mouse (10.1002/0471142301.ns0810as55). Please, justify the rats use in the present study.
Validity of the findings: The “n” experimental should be specified in each technique. For example, some graphics contains data points which facilitates the comprehension. However, for example, in the Western blot technique, there is not the “n” specification as well as the data points. Please, pay attention.
Additional comments: In the discussion section line 465-466 “This study found that stress not only caused the maternal rat’s exercise capacity to be weakened”. Please, specify the parameter analyzed for these affirmation: “as demonstrated in horizontal movement”.

The protein CREB develops a critical role in pathogenesis of depression. Although the authors measured the CREB levels in the hippocampus of rats, there are a poorly discussion about this protein and its relevance for the study. Here are some suggestions of studies to improve the discussion section: (10.1016/j.biopsych.2005.11.003; 10.1002/bies.20658; 0.1016/j.bbr.2021.113245; j.neuropharm.2022.108990; 10.1016/j.bbr.2018.04.050).

Should the authors provide a mechanistic possible explanation of how CUMS is causing emotional behavior impairment. The reviewer encourages the authors to include a figure or graphical abstract for better understanding.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: CONTRIBUTION OF HIPPOCAMPAL BDNF/CREB SIGNALING PATHWAY AND GUT MICROBIOTA TO EMOTIONAL BEHAVIOR IMPAIRMENT INDUCED BY CHRONIC UNPREDICTABLE MILD STRESS DURING PREGNANCY IN RATS OFFSPRING
Review round: 1
Reviewer: 3

Output:
Basic reporting: All comments are below
Experimental design: All comments are below
Validity of the findings: All comments are below
Additional comments: The manuscript entitled “Chronic stress during pregnancy causes impairments in
emotional behavior of offspring may through BDNF/TrkB signaling pathway and gut
microbiota (#71239)” evaluated the impairments on behavioral and molecular parameters
in offspring from stresses mothers (rats). Furthermore, it was evaluated the gut microbiota
alteration and its relation with stress on previous generation. The author found alterations
on several parameters in mothers that show the validation of stress protocol. Some
changes in brain and gut microbiota were found in adult offspring from stressed mothers,
when compared to control group.

The results are consistent but some alterations (described below) could be made in order
to improve the quality of manuscript.

1. The whole manuscript must be revised by a fluent speaker;
2. Line 27: replace “gravid” by “pregnant”;
3. Line 28: replace the sentence “..and body weight and behavioural changes
recorded.” By “.The body weight was measured and the behavioral changes were
recorded.”
4. Line 32: replace “chronic stress” by CUMS.
5. Introduction: Paragraph 1 is too long, please divide it into 3 parts. I suggest divide
it at first in line 55 finishing with Plant et al., 2016. The second part I suggest divide
it in line 61, after Rotem-Kohavi et al., 2020.
6. Line 143: The term “canthal vein” is correct?
7. Line 151: I think rats don't received 1mL of blood, but 1mL was collected from
rats? If yes, please correct.8. The authors propose that stress can cause behavioral alteration through
BDNF/TrkB signaling pathway and gut microbiota. But the discussion lacks
information about relation between these two aspects (BDNF/TrkB signaling
pathway and gut microbiota). For example: The stressful events lead to a
modification in gut which lead to alteration in hippocampus or the modification
in hippocampus lead to gut alterations? Or there was no influence between these
two parameters? Please include some sentence about it
9. Discussion: Which consequences can happen when the gut microbiota is
modified, please discuss it.
10. Figure 6: In the figure of Western blotting (6d-i) authors just showed means and
SD. But SD does not appear in control group (black bar). Maybe authors could
change the color of bar, for SD to appear.
11. In all figures there were showed individual measurements (dots). In figure 6 could
appear the individual spots too.
12. Figure 6: There are only two experimental groups. For more confidence in results,
it could appear more than one representative band (maybe two).
13. Figure 6: TrkB band are not much representative with bars in figure 6e.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: CONTRIBUTION OF HIPPOCAMPAL BDNF/CREB SIGNALING PATHWAY AND GUT MICROBIOTA TO EMOTIONAL BEHAVIOR IMPAIRMENT INDUCED BY CHRONIC UNPREDICTABLE MILD STRESS DURING PREGNANCY IN RATS OFFSPRING
Review round: 2
Reviewer: 1

Output:
Basic reporting: Clear and unambiguous, professional English used throughout.
Experimental design: Original primary research within Aims and Scope of the journal.
Validity of the findings: Conclusions are well stated, linked to original research question
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: GENOMICS ENTERS THE DEEP LEARNING ERA
Review round: 1
Reviewer: 1

Output:
Basic reporting: Authors provide a very good overview of deep learning architectures for functional annotation of genomes, prediction of the effect of non-coding mutations and generation of synthetic genomic sequences. The manuscript is clearly written and easy to follow.
Experimental design: The areas of interest, three in total, are clear and the selected publications are representative for these areas.  As a literature review paper, the methods are adequate.
Validity of the findings: Major issues:
The conclusions section is limited. What is the main outcome of the manuscript? What can we learn from the selected publication ? Which are the most common DL architectures used and why ?
I would propose to the authors to provide also a discussion section where the limitations of deep learning approached in genomics are presented and a commentary of the low adoption of DL models in the field compared to other computer science fields such as vision or NLP.
A couple of sentences in the abstract about the discussion/conclusions of the review must be added.
Additional comments: Minor/Typos:
91 “evaluated on cell types that different”  “evaluated on cell types that are different”


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: GENOMICS ENTERS THE DEEP LEARNING ERA
Review round: 1
Reviewer: 2

Output:
Basic reporting: The authors survey recent developments in the intersection of deep learning and genomics. As stated clearly in the introduction, the review is primarily for the benefit of biologists working in genomics and adjacent fields. The emphasis is on applications and results, rather than architectures and techniques, which distinguishes this review from others and makes it fit for PeerJ's scope.

They broadly group the contributions into three categories: annotation transfer, biological mechanism discovery, and synthetic genomics.

In annotation transfer, neural networks trained on experimentally-derived annotations in one context (environment, cell type, species) are used to generate annotations in another context (another environment, cell type, or species). For example, the authors discuss together the results of Khodabandelou et al. (2020), wherein transcription start site inference transfers across species, and the results of Quang and Xie (2019), wherein transcription factor binding site inference transfers across cell types.

In biological mechanism discovery, neural networks are probed (e.g. in their weights or in their predictions in response to perturbed inputs) and the outcomes interpreted in biological terms. For example, the authors share the results of Wang et al. (2018), wherein the learned weights in the first layer of a CNN trained to predict RNA binding sites were used to hypothesize a novel mechanism, later validated experimentally, for triple bond formation between double-stranded DNA and single-stranded RNA.

In synthetic genomics, neural networks are used to predict the features of entirely synthetic nucleotide sequences. For example, the authors share the results of Chuai et al. (2018) and Xue et al. (2018), which both use neural networks to optimize the single guide RNA sequences used to target gene editing using the CRISPR-CAS9 system.

This systematic review and its conceptual framework will no doubt be of great help to genomics researchers who want to navigate the difficult literature at the intersection of deep learning and genomics. As a deep learning researcher originally trained in biology, I benefited from exposure to several exciting new avenues of research at the juncture of two of my favorite fields.

However, there are multiple major issues that need to be addressed before the review is ready for publication.

Issue #1:

I find that the positioning and language in the review substantively misrepresent the strength and implications of the results in annotation transfer and in mechanism discovery. Both applications rely on the usage of neural networks in ways that are far from guaranteed to work. This is not obvious just by reading the review, which risks misleading non-ML experts into weighting evidence from deep learning models too highly, relative to other forms of evidence. In clinical medicine, initial enthusiasm for deep learning driven by over-stated results has given way to broad cynicism as the rubber has hit the road. See, e.g., Wynants et al. 2020 (doi: 10.1136/bmj.m1328) and Roberts et al. 2021 (doi: 0.1038/s42256-021-00307-0) for critical reviews of machine learning models for COVID-19 diagnosis.

Annotation transfer relies on what the machine learning field calls “out-of-distribution generalization”. While neural networks have been found to generalize in this way (for a contemporary review, see Shen et al. 2021, doi: 10.48550/arXiv.2108.13624), they also often fail, even catastrophically so, even in the easier case of in-distribution generalization (see adversarial examples: Wiyatno et al. 2019, doi: 10.48550/arXiv.1911.05268; and test set resampling: Recht et al. 2018, doi: 10.48550/arXiv.1806.00451), especially in the case of supervised methods, like most of those considered here. Unlike for classical machine learning methods, like linear models, theoretical or engineering guarantees on generalization behavior of neural networks have been elusive (classic result in Zhang et al. 2017, doi: 10.48550/arXiv.1611.03530). Even the problem of determining when a model is failing to generalize on closely-related data is challenging (for some empirical results see Rabanser et al. 2019, doi: 10.48550/arXiv.1810.11953; for recent work on limits to performance, see Ginart et al. 2022, doi: 10.48550/arXiv.1611.03530).

The review should make it clear that any annotations by a network with no exposure to the target domain are of a highly tentative nature – more akin to the results of a GWAS than of a mechanistic study (to say nothing of a pre-registered study or a body of literature). Further, they should note the common solution to this problem: fine-tuning models. Fine-tuned models are first trained on a closely related domain with rich data and then trained again on the target distributions where data is limited (for review see Iman et al. 2022, doi: 10.48550/arXiv.2201.09679). For example, one could train a model in the richly annotated context of HeLa cells and then, given a much smaller dataset of annotations in a data poor context, like cells in a newly-discovered species, fine-tune the model to give reasonable annotations in the new domain. Importantly, this means that the model performance in the target domain can be assessed on held-out test data from that domain. For more detail, see the “Validity of the Findings” section.

The results in biological mechanism discovery share these issues and add more. That is because mechanism discovery relies in some cases on out-of-distribution generalization, e.g. when the network’s inputs are perturbed and the predictions interpreted in biological terms. because this is a form of out-of-distribution data, the same caveats from annotation transfer apply. In these cases, mechanism discovery should be framed as a hypothesis generation tool to speed up research by suggesting targeted experiments (again, much like a GWAS).

Other forms of biological mechanism discovery involve “opening the black box” of the network and trying to learn, as scientists, what the model has learned. The interpretability methods used to obtain some of these results (gradient-based attributions, Shapley values) are very controversial in the deep learning community. Contrary references must be included in the “Opening the black box” insert alongside the explanations of the methods and a hint of the unsettledness of this science should appear in the text. For more details, see the “Validity of the Findings” section.

Note that the use of neural networks in synthetic genomics has the same issues, but the use cases presented in the review take this into account. For example, neural networks are positioned as a method for generating candidate CRISPR guide RNAs, with experimental verification.

Issue #2:

I noticed a large number of small factual errors in the text, described in the “Validity of the Findings” section. They include mathematical, computational, and conceptual errors. Because I am an expert only in the deep learning half of deep learning for genomics, I can only comment on the errors there, but they do not inspire confidence in the material in which I am not an expert.

I urge the authors to carefully review all their claims about presented results, not just the ones identified in my review, before resubmission.

Issue #3:

Finally, there are a number of issues of language (spelling, grammar, and comprehensibility) in the manuscript and of type-setting and organization in the tables. The tables in particular are a key contribution of the paper, as they help interested readers navigate the literature quickly at a high level, so their organization and formatting is important.

I have identified these issues in detail in the “Additional comments” section.
Experimental design: The methodology, based on searching PubMed and similar online databases, is simple but effective. There are no quantitative claims about the literature in the text, so there is no need for a more sophisticated review strategy.

The organization of the literature into three broad classes based on application is clean, clear, and of great utility to researchers interested in new approaches to long-standing genomics problems.
Validity of the findings: My most substantive concerns with the manuscript, on which I am basing my request for major revisions, regard the validity and factual correctness of the content.

As described in the “Basic Reporting” section, the largest issues concern overstating the results based on out-of-distribution generalization and model interpretation. I will here pick out specific areas where I think .

On line 96, the authors mention that Liu et al. (2019) test in “the same stress condition as their training” and note that this “fail[s] to firmly demonstrate” out-of-distribution generalization. The language here should be much stronger. There is no way, from the given results, to evaluate whether the model in question generalizes to other stress conditions. I would suggest moving this result to the top of the annotation transfer section and pointing out this methodological issue and the ways in which it is avoided (and not avoided) in other results. For example,  Minnoye et al. (2020) have strong evidence for a model that generalizes across species but don’t provide evidence for generalization across cell types, since all samples are from melanoma cell.

On lines 130-132, the authors say “experimental cues may be needed in some case [sic] to confirm or not [sic] the computational predictions”. This presents external validation of out-of-distribution results as the exception, rather than the rule. Instead, it should be stated that the computational predictions are just one line of evidence about the true annotation. The strength of that evidence depends on how well the model has predicted out-of-distribution instances in the past and how different the distribution of interest is to the training distribution and to other tested distributions. The prospect of a small dataset of experimental annotations being used to quickly transfer models should be explicitly discussed, since that is where deep learning experts and genomics experts can collaborate most fruitfully to expand the scope of annotated genomes.

On lines 134-136, the authors say “In a metaphoric [sic] way, neural networks are trained to emulate the mechanistic link between the DNA molecule and the cell nucleus machinery, including TFs, that "read" the DNA sequence. The biological complexity is replaced by the complexity of the neural network.” This is a beautiful and potentially useful metaphor but it dangerously overstates the alignment between what neural networks learn and biological fact. It gives, along with the claims in the following section, the impression to non-experts that neural network predictions can be trusted as though they were the outcomes of biological experiment

A better way to make a similar rhetorical move without the overstatement might be to say: “We’ve seen above that when trained appropriately on large datasets, neural networks are capable of predicting annotations in new contexts. These neural networks in some ways mimic the machinery of DNA binding: metaphorically, both “read” the DNA sequence and “annotate” it, with labels or molecules. Such a neural network can be treated as a rich model of that DNA binding machinery and used for in silico experiments to generate hypotheses and theories, like any other model.”

On line 290, the authors claim that one of their studies on nucleosome positioning “demonstrated that attractive motifs are unlikely to exist”. In reviewing the paper (doi: 10.1101/gr.264416.120), I only found a much weaker claim: ”we do not find any motifs that would position nucleosomes by attracting them”. As I am not an expert on the genomics here, I would appreciate a clarification of how the much stronger biological claim is supported.

As mentioned in the “Basic reporting” section, there has been much research into the shortcomings of the attribution methods used for some of the mechanism discovery results. For concerns about the objectivity of Shapley-style methods, see Sturmfels et al. 2020, doi: 10.23915/distill.00022, and the excellent references therein. In particular, Adebayo et al. 2018 (doi: 10.48550/arXiv.1810.03292) demonstrate that some saliency methods can report the same results even when weights are randomized or when the model is trained with random labels.

There are also a number of places where results from other papers are described in purely qualitative terms (“is relevant”, “performs correctly”) rather than quantitative terms (“achieves accuracy five percentage points higher than a BLAST baseline”, “reduces the number of false positives by a factor of 10, relative to traditional methods”, “achieves comparable accuracy with only”). These kinds of quantitative comparisons are more common for evaluating models in the deep learning literature and should also be more immediately interpretable to an expert genomics audience.

Some examples of qualitative results are listed below, with line numbers.

87. “predictions are relevant”
91. “studies shows [sic] promising results”
115. “performs correctly on all the other species”
183. “variations in network predictions were found to be significantly higher”
194. “show the consistency between”
258. “show its utility in understanding”
259. “confirmed the relevance”
354. “led to unusual level of expression”.

Expanding on line 354, which is regarding Vaishnav et al. (2021)’s transformer-validated, synthetically evolved promoter sequences: were unusual levels the desired outcome? How is “unusual” quantified? Why is it not “desired” levels of expression? Are there benchmark comparisons, e.g. to human-designed promoters?

Some smaller points:

On line 228, the authors indicate that Zhang and Hamada (2018) identified a “new” m6A reader “FMR1, which was unknown to date”. This is incorrect. Zhang and Hamada indicate that FMR1 was determined to be an m6A reader via traditional methods in a paper published the previous year: Edupuganti et al. (2017), doi: 10.1038/nsmb.3462.

On line 282, the authors reference Zeng et al. (2018)’s “NLP-inspired” model. In that paper, they used a shallow, Word2Vec-style embedding layer and gradient-boosted treess. These are NLP-flavored methods but they are not deep learning methods, because there is no stacking of differentiable modules. This paper and its results should be removed from the review.

In the “Sequence classification” insert, the following claim appears: “Indeed, once a network is trained to attribute a species to a DNA sequence, the prediction will always take the same amount of time.” While this is true, it is a somewhat unfair comparison. BLAST gets better at classification with each added reference species, while a neural network only gets better when it is retrained. That retraining process is much more complicated and error-prone than adding a new sequence for BLAST. For large neural networks, retraining can also take several days.

Regarding the “Opening the black box” insert:
The equation in this section is incorrect. The left-hand side is an exact quantity and the right-hand side is the first-order term of the Taylor expansion for approximating that quantity. These two quantities are not in general equal. Either the higher-order terms must be represented (using Landau notation, adding + O(X - X0)) or, since this is a less technical audience, the equation should be replaced with an approximate equals sign (e.g. \approx in LaTeX).

Additionally, it is not entirely correct that the derivative of the class with respect to the input is computed during network training. During training, we calculate the derivative of the loss with respect to the parameters (typically, weights and biases of linear layers). It would be more correct to say “this quantity can be easily and cheaply computed with the same methods used during network training”.
Additional comments: The grouping of research is great and a wonderful contribution, but the second section, which I’ll call “biological mechanism discovery”, after its identifier in the tables, is somewhat confusingly presented. In the abstract (line 13), it’s “prediction of the effect of non-coding mutations”. In the end of the introduction (lines 53-54), it’s “getting a deeper understanding of the molecular mechanisms”. In Figure 2’s caption (above line 61) it’s “get mechanistic insights on a process involving the DNA sequence”. The title of the relevant section (line 133) is “Unveiling sequence determinants of genomic annotations”. This makes it a bit harder to see how the authors have split the literature cleanly into groups. I recommend unifying the language (e.g. by consistently using the phrase “biological mechanism” in each place).

What an ML researcher would call a “network” or “model” is here referred to as a “methodology”. If this is standard in the world of deep genomics, that doesn’t have to change, but even then it would be better to align with the broader world of ML.

143. Both inserts are labeled “insert 3”. There are only two inserts.
148. “covers protein functions and will not be discussed here”. A brief comment on the difference between genomics and genetics and a citation for applications of deep learning to the latter would be appropriate here.
197. “study uses 7097 genomes including 1790 siblings”. Are all of the genomes from siblings or just some? The following description leaves this unclear to me.
215. “learned by itself” is excessive anthropomorphization of the ML algorithm. Suggest deleting “by itself”.
222-223. “17 filters” and “13 others” needs more context. How many filters are there total? “17 filters out of X” would be sufficient

“filters” (line 210) and “in silico mutagenesis” (line 214) are used without definition, but they are defined in the “Opening the black box” insert. Readers should be directed to the insert for the definition.

The following terms are not defined:
129. “over-fitting”
130. “generalisability” (which should be generalisation)
304. “pre-trained”.
In “Black box” insert: “regularization” and “cost function”. I would suggest instead “An alternative option is to adjust the networks’ training procedure to penalize the use of patterns that are too small”, which avoids introducing the two technical terms.
In “Black box” insert: “one-hot encoded”. If the authors think that this term is known among genomics researchers, it need not be defined, but it is italicized, so perhaps they intended to define it.

The manuscript might be improved by the following citations:

46. For CNNs in computer vision: the Neocognitron (Fukushima 1980, doi: 10.1007/BF00344251) and LeNet (LeCun et al. 1989, doi: 10.1162/neco.1989.1.4.541).
266. A citation for the role of Kozak sequences around AUG codons
273. “the gradient method” – which? Add relevant citation. Should also direct readers to the relevant insert.
281. “attribution maps” – which? Add a citation. Again, direct readers to the relevant insert.
315. A citation for CRISPR.

Suggestions for improvements to the tables:

The architecture column has inconsistent levels of detail, e.g. in “self-attentive gated convolutional highway” for Guo et al. (2021) versus “CNN” for many others. I would change it to “Architectural Features” and pick them from a small list: “convolutions”, “recurrence”, and “attention” is enough.
The “preprocesing” column seems unnecesssary. Almost all methods use a one-hot encoding method, especially in Table 1. If it is critical to include, could add “embeddings” to the list of “Architectural Features”.
The second page of Table 1 is split into two pieces, but it’s unclear why.
Table 2 and Table 3 have the same columns and title. Why are they separately numbered tables?
The spacing and placement of the words is distractingly poor, e.g. where Guo et al. (2021)’s “Annotation” column entry crosses over
The capitalization and spellings of many of the words are inconsistent: “Syn.” vs “syn”, “genomic” vs “genomics”, “Tranfer” in place of “Transfer”

Below are detailed corrections to the grammar, spelling, and language in order of line number. To be clear, these are suggestions, and if the authors disagree on any, they are welcome not to incorporate that particular change and there is no need for a detailed response. However, the manuscript is generally in need of copy and prose editing to improve clarity.

24. “publication” should be “publications”
36. Missing right parenthesis after “(2018)”
43. “Among the first and emblematic” should be “most emblematic”
55-56. “classification methodologies such as machine learning” is incorrect. ML encompasses more than just classification. Suggest “from” in place of “such as”
73. “second in the transfer” should be “second is the transfer”
74. “network was trained and another species” should be “network was trained to another species”
79. “can help filling this lack” is incorrect. Suggest “can help address this lack”
85. “across human cellular context” should perhaps end in “contexts”
87. “inverstigated” should be “investigated”
89. “cell type specific” should be “cell type-specific”
91. “cell types that different from” should be “cell types that differ from” or “cell types different from”
91. “two studies shows” should be “two studies show”
95. “472 TF and signal molecules binding context” should perhaps end with “binding contexts”
110. “enhancers prediction” should be “enhancer prediction”
118. “”generalisability” should be “generalisation”
119. “non coding variant” should be “non-coding variants”
128-129. Sentence “The first is the lack of conservation of molecular mechanisms at work between species that are too far away on the tree of life.” is awkwardly constructed. Suggest “The first is that the further away species are on the tree of life, the less conserved are molecular mechanisms.” or similar
131. “experimental cues may be needed in some case” should end in “cases”
141. “i.e” should be “i.e.”
142-143. “described in the insert 3” should be “described in Insert 3”
146. “Single Nucleotide Polymorphism” should be “Single Nucleotide Polymorphisms”
147. “within the coding sequences” should be “within coding sequences”
149-150. In “their mechanistic role is often difficult to comprehend.”, “comprehend” is a strange word choice. Suggest rewriting “a mechanistic role cannot be immediately inferred.”
150. “also know as Genome Wide Association Studies” should be “also known as a Genome Wide Association Study”. The appositive refers to “the statistical approach”, so the plural is not correct.
151. “These analysis” should be “These analyses”
153. “GWAS are” should be “GWAS is”. While it is true that GWAS is sometimes used as a plural, in this context the text is referring to the statistical approach, singular.
156. “data-sets” should be “datasets”
163. “,it” is missing a space after the comma
169. “data-sets” should be “datasets” and “test-set” should be “test set”
182. “with the former” could be shortened to “with them"
191. Missing year in citation of Wesolowska et al.
194. “show the consistency” should perhaps be “show consistency”
196. “A practical application of variant analysis using neural networks is provided by Zhou et al. (Zhou et al. (2019)) to study variants related to autism.” This intro line should be re-written. See comment below on lines 204-207.
199. “the other are” should be “the others are” or “the other is”, depending on whether the sibling groups are always pairs or are sometimes larger.
199. “Studying siblings allows to compare the effect of SNPs in genetically very close individuals” is grammatically incorrect. Suggest removing, because the role of sibling studies should be clear to anyone with interest in genomics
201. “The difference is even reinforced when” should be “The effect is larger when”
204-207. The sentence starting “Illustrating the” is something of a non-sequitir, since the rest of the paragraph is organized around relating a different result. Suggest starting the paragraph with “A number of biomedical studies have demonstrated practical application of variant analyis and SNP interpretation.”, then quickly explaining the Richter et al. (2020) results, then explaining the Zhou et al. (2019) results.
208. Section heading ends with punctuation, unlike other headings
210. When you first introduce filters, point to “Opening the black box” insert for definition.
212. “sites were experimentally validated” is a mis-use of passive voice. It should be clear whether the experimental validation was done by the same group that made the prediction, even if it was a posteriori
218. “from the sequence” should identify which sequence (DNA? RNA? something else?). From context it is clear that it’s DNA, but there are lots of sequences. Note that the phrase “DNA sequence” is used e.g. on line 286 but this is not done consistently.
229. Section heading ends with punctuation, unlike other headings
231. “associated with 20 millions” should end with “20 million”
231. “80-bp long sequence” should be “80-bp long sequences”
231. “s.cerevisiae” should be capitalized. Typical style is to use the whole Linnaean name the first time: “Saccharomyces cervisiae”.
232. “promoters regions” should be “promoter regions”
234. “mutation have” should be “mutations have”
243.  “5’UTR” uses a different mark here than elsewhere
245. “poly-A” is written differently here than elsewhere. Should perhaps define as “polyadenylated” in this first use
250. “see the insert 3” should be “see Insert 3”
252. “kpb” should be “kbp”
257. “from the sequence”: which? See comment on line 218
261. comma at the end of “canonical AUG codon”
263. deep learning is italicized here and in several other places when it should not be
265. Kozak sequences are referred to as “so-called” here but not in their first introduction. Suggest only using “so-called” at the first introduction or not at all
268. deep learning s italicized here and in several other places when it should not be
273. “Arabidopsis Thaliana” should be “Arabidopsis thaliana”
277. “CTCF” is not defined here, which is the first usage
282. “NLP” should be “natural language processing” at first usage
284. “the loop forming activity” should be just “loop-forming activity”
286. “the nucleosome positioning” should just be ”nucleosome positioning”
291. “the nucleosome positioning” should just be ”nucleosome positioning”
291 “from the DNA sequence, more precisely” should be “from the DNA sequence. More precisely,”
292. “the result of the MNase-seq protocol considering that” is an awkward construction. What precisely are they predicting and how exactly is it related to nucleosome positioning and TFBS?
294. “competition between nucleosome and TF” should be either “the nucleosome and TFs” or “nucleosomes and TFs” – I believe the latter
302. “but also its position” occurs without an accompanying “not only” or “not just”. Unclear from context what should go here, because “refining the sequence to introduce” is an incomplete fragment.
309. “perspective” does not seem to be the right word here. Is it “related work” or “a related approach”?
312. “great promises” should be “great promise”
314 “the combination of the CRISPR-Cas9 protocol” is incorrect – if you’re not referring to separate protocols, there’s nothing to combine. I believe “the CRISPR-Cas9 protocol” is correct here
321-323. Instead of putting the issue of IP and security first, put the Nielsen et al. (2018) reference first, then explain why it’s important.
324. “from sequence” should be “from their sequences” or “from their DNA sequences”. SSee comment on line 218
325. Is Nielsen et al. on line 323 the work cited here as “Nielsen and Voigt (2018)”? These should both be Nielsen and Voigt.
325. deep learning is italicized here and in several other places when it should not be
328. “exciting perspective of the application” is incorrect. I believe it should be “exciting prospective application”
332. deep learning is italicized here and in several other places when it should not be
333. There is an extra right parenthesis after “deep learning”
336. “developed Ledidi a methodology” should be “developed Ledidi, a methodology”
341. “Going further these applications on TFBS” is ungrammatical
345. “to alternately determine” should be “to both determine”
347. “MRPA-trained CNN” is the first appearance of MRPA, so the acronym should be defined
351. “s.cerevisiae” should be capitalized to “S. cerevisiae”
351. “to evolve 80-bp long sequence toward a sequence that” should perhaps be “to produce an 80-bp long sequence that”. This is shorter and avoids confusion with in vitro evolution
352. “a sequence that induce” should be “a sequence that induces”
353. “associated to 20 millions” should end with “20 million”
354. “The” is unneeded at the beginning of the sentence
358. “bioinformatic” should be “bioinformatics”
359. “between cell type” should be “between cell types”
359. “amounts of genomes” should be “number of genomes”
361. “We have also shown the potential” seems incorrect. That phrasing is usually used to refer to reported experimental findings. Instead, perhaps “We have also reviewed demonstrations of the potential”
363. “the most transformative application of deep learning, which is the generation” could be shortened to “the most transformative application of deep learning: the generation”
366. “will and already has” is ungrammatical.
366. “bioinformatic” should be “bioinformatics”
366-367. “apologies to the colleagues whose work was not referenced here, even if we did our best to cover this vast and rapidly evolving field.” is not a great ending. First, if this is truly a systematic review, there’s not much to apologize for. Second, it’s tonally quite different from the rest of the manuscript. Finally, it’s a weak foot on which to end. I would suggest removing this sentence and ending with a statement about the excitement and possibilities of the “deep genomics era”.

Note: there are no line numbers in the insert, so the remaining edits are not placed on a particular line but instead numbered in order of appearance.

“Sequence classification” insert

“approaches should allow to overcome this problem.” should be “approaches do not suffer from the same problem.”
Last sentence in Species classification section: “great promises” should be “great promise”
“Luo et al. developped” should be “Luo et al. developed”

“Opening the black box” insert

“coined as” is incorrect. Change to “referred to as” or “considered”
The definition “The interpretation of neural networks is defined here as a set of analysis that reveals, at least partially what are the important features that are recognized by the network to make a correct prediction.” feels out of place, since the term “interpretation” isn’t used very much and the definition isn’t particularly precise.
“a set of analysis” should be “a set of analyses”
“at least partially” should end in a comma
“genomic networks” is confusing – it could refer to networks of interactions at the genome level. Change to “deep networks in genomics” or just “deep networks”, since these methods are all general.
In the final paragraph in the first section of this inset, two sentences are fragments: the one beginning “First-order methods, that” and the one beginning “Second-order methods, that”. Removing “, that” would fix this issue.
“elementary multiplication” should be “elementwise multiplication”
“patterns with its first layer this information can be” should be split into two sentences: “patterns with its first layer. This information can be”
“dispersed within all layers by successive combinations” is unclear. I suggest removing “by successive combinations”.
“To overcome this criticism” feels incorrect. It is not a criticism but instead a “limitation” of neural networks.
“CNN” and “RNN” should be pluralized to “CNNs” and “RNNs”
“second class of methods is to assign” should be “a second class of methods assigns”
in silico mutagenesis is finally defined here after being used in the text multiple times. At its first usage, point the reader to this insert.
I was unable to understand the explanation of gradient-based methods: “The second method is based on the estimation of the change in the prediction Pc(X0), obtained for the class c, that would be obtained in the input sequence changes from X0 as input to X.” What does it mean to have an “input” to X, which is not a function?
“gives the equation 1:” is incorrect. First, the definite article is not needed. Second, there is only one equation in the paper, so there’s no need to call out which it is. Third, the colon is used to direct the reader to the equation, so a number is superfluous. I suggest “gives:” and removing the annotating (1) from the equation.
“allows to obtain” is incorrect. Change to “produces”
The last two sentences of the first paragraph of the “Second order – Grammar of motifs” section are fragments: the one beginning “First methodologies, that” and the one beginning “Second. methodologies, that”. Removing “, that” would fix this issue.
“the attention mechanism” is incorrect, as there are multiple types of attention mechanism. Change to “an”.
“one-hot encoded” is italicized and should not be
Last paragraph: “An other” should be “another”
“the grammar of motif, is” should be “the grammar of motifs is”
“in which the motif artificially included” should be “in which the motif has been artificially included”.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: SPECIMEN ALIGNMENT WITH LIMITED POINT-BASED HOMOLOGY: 3D MORPHOMETRICS OF DISPARATE BIVALVE SHELLS (MOLLUSCA: BIVALVIA)
Review round: 1
Reviewer: 1

Output:
Basic reporting: This paper addresses a problem well-known to morphometricians working with bivalves in a broad phylogenetic context, i.e., how to produce meaningful shape normalizations when a limited number of homologous points are available. The authors start by discussing anatomical features of the bivalve shell that have been traditionally used to define body axes and allow its standardized orientation. These axes and features are used to devise, implement and quantitatively compare a number of strategies for aligning the bivalve shell using 3D morphometrics (including a number of novel alternatives to standard procedures) and a sample of shells encompassing the diversity of body plans of the bivalvia. The paper closes with a discussion of the biological meaning of the differences found between approaches and the potential adequacy of the different strategies studied.

This is a very clear, well-written, self-contained study. The authors succeed in providing conceptual, historical and technical context for the reader, and the figures (and table) provided are fit for purpose. While the paper slightly departs from the standard sections of the journal, I think its structure is appropriate for addressing the issue at hand. The authors share both the raw data and the code they used, and include supplementary information that is very useful to delve into the specifics of their methodology.
Experimental design: This piece of research falls well within the scope of PeerJ. The issue being tackled (which is relevant primarily for morphometricians and bivalve researchers, but more generally for any researcher working with structures with few or no discrete homologue points) and its importance are adequately explained and framed. Throughout the article, the authors explore strategies specifically devised to mitigate the problem addressed, making an effort to insert these within the corresponding body of knowledge. Their anatomical discussion (and related literature) and methodological descriptions (and associated R code) stand out as particularly valuable contributions to the topic (in fact, I was wondering if the approach to morphometric quantification used here has been published elsewhere–if not, maybe the related supplementary text and figures could be relocated into the main text).

The rationale of the paper is sound and the explanations of the morphometric procedures undertaken are clear and very detailed, needing only minor corrections, clarifications and/or additions (see attached PDF). Regarding reproducibility, I was not able to run the script “Edie_etal_bivalignment” beyond line 403 because the function “points_along_plane” was missing (also, the script with helper functions does not run beyond its line 472 when called with source, I had to do it manually). However, as far as I could see this material is excellent and would be extremely useful for anyone interested in understanding and replicating this approach. It would be great if the authors could fix these problems and provide a fully functional script.
Validity of the findings: I was able to download and check both the raw data and code (stored in Zenodo), although as mentioned earlier I was not able to replicate all the results reported in the paper due to the lack a function. In any case, all the results and findings of the article seem valid and stemming from sound reasoning and adequate application of methodology (my only concern in this regard would be the results from perMANOVA, as I am not sure the function chosen by the authors is the right alternative to perform this particular analysis; see comments in page 16 of the attached PDF). The conclusions arrived at are all supported by the results and discussion, and are adequately aligned with the research goal.

Pablo Milla Carmona
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: SPECIMEN ALIGNMENT WITH LIMITED POINT-BASED HOMOLOGY: 3D MORPHOMETRICS OF DISPARATE BIVALVE SHELLS (MOLLUSCA: BIVALVIA)
Review round: 1
Reviewer: 2

Output:
Basic reporting: English is perfect.  Literature references are insufficient as noted in my review. The paper is not self-contained (it fails to review the axioms of GMM).
Experimental design: The paper is original.  Its question, however (how to adapt geometric morphometrics to bivalve macrotaxonomy) is not meaningful.
Validity of the findings: The authors' basic conclusion, as per the Abstract sentence my review quotes, is correct, but the paper should have had the courage to say that any other expectation represents a profound misunderstanding of the actual logic of geometric morphometrics, which has no role to play here.
Additional comments: See attached .pdf of a full review text.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: SPECIMEN ALIGNMENT WITH LIMITED POINT-BASED HOMOLOGY: 3D MORPHOMETRICS OF DISPARATE BIVALVE SHELLS (MOLLUSCA: BIVALVIA)
Review round: 1
Reviewer: 3

Output:
Basic reporting: The article has been written in clear, unambiguous and very professional English. Also, the article includes very nice figures and a table, and a very useful amount of supplementary data.

The authors share links to the raw data.

Figures are relevant and properly labeled and described.

The article is well structured, including acceptable and standard sections. However, I suggest a more clear separation between results and discussion (see comment in ms). This could improve how you show the extension of your results for a broad audience and future applications. So, this does not mean separation in two sections (Results-Discussion), I propose rephrasing some sentences about discussion (those referring the focus of your study, recommendations for use, goals, extension of the problem, etc.) of the sub-section “Practical alignments for bivalve shells”.  Also, the issue about extension of the methodology to other model systems is not developed in the discussion and I hope to see it in the discussion (see comments in ms).
Experimental design: This is an original, well defined, relevant research, focusing in a becoming problem associated to the methodologies used today.

This research is clearly defined in its focus, and the contribution to the field is clearly defined and delimited.

Methodology are clearly described with sufficient details, is rigorous and replicable from the information and data provided. So, fine details of the methodology are beyond my knowledge and expertise.
Validity of the findings: Results of this contribution are novel, and to assess with pertinent tools a problem related to methodologies with a broad use.

As I said above, the extension of the results are briefly mentioned.

Decisions are not subjective, they are based on results. All needed data to replicate are provided and are available.

Conclusions are well stated.
Additional comments: The article assesses a very interesting problem concerning to a broad used methodology today, in palaeontology and morphology. Details of the methodoloy are beyond of my expertise, but the methodological bases and procedures are well worked. One of the goals of this work is to link biological/anatomical basis of different approaches with methodological usages, based on the revision of morphological nomenclature and the morphology itself. I hope to see this article published.

The following lines are related to my comments on the ms:

The introduction are short and concise, but some words about the disparate body plans (examples) are needed. Also, a more detailed explanations about the advantages and disavantages on the use of shells would be adequate. They considered living and extant taxa as a continuous and I agree with this. So, these results are important to palaeontologists and morphologists equally. The use of shells is an advantage for it, and for the facility to access, availability in collections, etc.

Figure 1 needs indications of specific parts. Only "a" and "b" are insufficient. This figure contains a lot of information explained in the text, and more indications help to the reader.

Dataset needs abbreviatures of collections used. This is referred to acknowledgements on Methods section but is not totally provided, more collections are indicated in the Table S1. I suggests they be included in the supplementary text.

Results and Discussion section is correct, but the sub-section "Practical alignments for bivalve shells" has some details: some lines seems to be recommendations or preferences. I encourage to authors to be more clear about this. Please provide recommendations and/or arguments on the results.

I suggest to include in the Results/Discussion section some lines about the extension of our phylosophy of alingment to other model systems. This is mentioned at the abstract and the conclusions, but not developed in the core of the article.

A line in the supplementary text refers to "Dijkstra's algorithm" but this is not referenced.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: SPECIMEN ALIGNMENT WITH LIMITED POINT-BASED HOMOLOGY: 3D MORPHOMETRICS OF DISPARATE BIVALVE SHELLS (MOLLUSCA: BIVALVIA)
Review round: 2
Reviewer: 1

Output:
Basic reporting: The original version of this manuscript was an already clear, self-contained, nicely written study tackling a  relevant issue in bivalve morphometrics (although of potential interest to researchers of other groups too), providing adequate figures, table and literature, and potentially useful digital materials and code (although the latter needed fixing). The current version of the draft improve most of these aspects further, as the necessary clarifications and corrections have been incorporated by the authors.
Experimental design: All the aspects in need of revision have been taken care of. I have only one fairly minor comment on the manuscript regarding lines 285-289:

After reading your responses and corrections, I realized that by 'correlation' you were referring to allometric variation (which es what Zelditch 2012:13 is taking about too) and not 'methodological independence' as I thought initally. I think that for the sake of clarity you should keep it simple and just be explicit about allometric variation (for example you could just modify the original sentence to mention allometric variation instead of correlation, e.g., “Shell volume–the amount of calcium carbonate–may be associated with allometric patterns different from those associated with the other two size measures, and therefore reveal other aspects of shape variation” or something along those lines.)

Aside from that, the R code is now almost fully functional (in line 62 of the script the closing parenthesis of “if” is misplaced and in line 1979 there is a comma lacking, although these are of course minor details that can be fixed easily by those checking this material by themselves), which is great for people interested in replicating this approach.
Validity of the findings: The only issue I was worried about (the PERMANOVA/Procrustes ANOVA one) has been clarified.
Additional comments: No additional comments