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Latest preprint reviews

1. Genome assembly of the milky mangrove Excoecaria agallocha

   This article has 26 authors:

   1. Hong Kong Biodiversity Genomics Consortium
   2. Project Coordinator and Co-Principal Investigators
   3. Jerome H.L. Hui
   4. Ting Fung Chan
   5. Leo L. Chan
   6. Siu Gin Cheung
   7. Chi Chiu Cheang
   8. James K.H. Fang
   9. Juan Diego Gaitan-Espitia
   10. Stanley C.K. Lau
   11. Yik Hei Sung
   12. Chris K.C. Wong
   13. Kevin Y.L. Yip
   14. Yingying Wei
   15. DNA extraction, library preparation and sequencing
   16. Sean T.S. Law
   17. Wai Lok So
   18. Genome assembly and gene model prediction
   19. Wenyan Nong
   20. Genome analysis and quality control
   21. Wenyan Nong
   22. Sample collector and logistics
   23. David T.W. Lau
   24. Sean T.S. Law
   25. Shing Yip Lee
   26. Ho Yin Yip

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment:

     This work is part of a series of papers from the Hong Kong Biodiversity Genomics Consortium sequencing the rich biodiversity of species in Hong Kong. This example assembles the genome of the milky mangrove Excoecaria agallocha, also known as blind-your-eye mangrove due to its toxic properties of its milky latex that can cause blindness when it comes into contact with the eyes. Living in the brackish water of tropical mangrove forests from India to Australia, they are an extremely important habitat for a diverse variety of aquatic species, including the mangrove jewel bug of which this tree is the sole food source for the larvae. Using PacBio HiFi long-reads and Omni-C technology a 1,332.45 Mb genome was assembled, with 1,402 scaffolds and a scaffold N50 of 58.95 Mb. After feedback the annotations were improved, predicting a very high number (73,740) protein coding genes. The data presented here provides a valuable resource for further investigation in the biosynthesis of phytochemical compounds in its milky latex with the potential of many medicinal and pharmacological properties. As well as increasing the understanding of biology and evolution in genome architecture in the Euphorbiaceae family and mangrove species adapted to high levels of salinity.

     This evaluation refers to version 1 of the preprint

   Reviewed by GigaByte

2. Whole genome assembly and annotation of the King Angelfish (Holacanthus passer) gives insight into the evolution of marine fishes of the Tropical Eastern Pacific

   This article has 5 authors:

   1. Remy Gatins
   2. Carlos F. Arias
   3. Carlos Sánchez
   4. Giacomo Bernardi
   5. Luis F. De León

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment:

     The King Angelfish (Holacanthus passer) is a great example of a Holacanthus angelfish that are some of the most iconic marine fishes of the Tropical Eastern Pacific. However, very limited genomic resources currently exist for the genus and these authors have assembled and annotated the nuclear genome of the species, and used it examine the demographic history of the fish. Using nanopore long reads to assemble a compact 583 Mb reference with a contig N50 of 5.7 Mb, and 97.5% BUSCOs score. Scruitinising the data, the BUSCO score was high compared to the initial N50’s, providing some useful lessons learned on how to get the most out of ONT data. The analysis suggests that the demographic history in H. passer was likely shaped by historical events associated with the closure of the Isthmus of Panama, rather than by the more recent last glacial maximum. This data provides a genomic resource to improve our understanding of the evolution of Holacanthus angelfishes, and facilitating research into local adaptation, speciation, and introgression of marine fishes. In addition, this genome can help improve the understanding of the evolutionary history and population dynamics of marine species in the Tropical Eastern Pacific.

     This evaluation refers to version 1 of the preprint

   Reviewed by GigaByte

3. De novo transcriptome assembly and genome annotation of the fat-tailed dunnart ( Sminthopsis crassicaudata )

   This article has 6 authors:

   1. Neke Ibeh
   2. Charles Y. Feigin
   3. Stephen R. Frankenberg
   4. Davis J. McCarthy
   5. Andrew J. Pask
   6. Irene Gallego Romero

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment: Marsupial species are invaluable for comparative studies due to their distinctive modes of reproduction and development, but there are a shortage of genomic resources to do these types of analyses. To help address that data gap multi-tissue transcriptomes and transcriptome assemblies have been sequenced and shared for the fat-tailed dunnart (Sminthopsis crassicaudata), a mouse-like marsupial that due to is ease of breeding is emerging as a useful lab model. Using ONT nanopore and Pacbio long-reads and illumina short reads 2,093,982 transcripts were sequenced and assembled, and functional annotation of the assembled transcripts was also carried out. Some addition work was required to provide more details on the QC metrics and access to the data but this was resolved during review. This work ultimately producing dunnart genome assembly measuring 3.23 Gb in length and organized into 1,848 scaffolds, with a scaffold N50 value of 72.64 Mb. These openly available resources hopefully provide novel insights into the unique genomic architecture of this unusual species and provide valuable tools for future comparative mammalian studies.

     This evaluation refers to version 1 of the preprint

   Reviewed by GigaByte

4. Molecular Property Diagnostic Suite for COVID-19 (MPDS ^COVID-19 ): An open access disease specific drug discovery portal

   This article has 19 authors:

   1. Lipsa Priyadarsinee
   2. Esther Jamir
   3. Selvaraman Nagamani
   4. Hridoy Jyoti Mahanta
   5. Nandan Kumar
   6. Lijo John
   7. Himakshi Sarma
   8. Asheesh Kumar
   9. Anamika Singh Gaur
   10. Rosaleen Sahoo
   11. S. Vaikundamani
   12. N. Arul Murugan
   13. U. Deva Priyakumar
   14. G.P.S. Raghava
   15. Prasad V. Bharatam
   16. Ramakrishnan Parthasarathi
   17. V. Subramanian
   18. G. Madhavi Sastry
   19. G. Narahari Sastry

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment:

     MPDSCOVID-19 has been developed as a one-stop solution for drug discovery research for COVID-19, running on the Molecular Property Diagnostic Suite (MPDS) platform. This is built upon the open-source Galaxy workflow system, integrating many modules and data specific to COVID-19. Data integrated includes SARS-CoV-2 targets, genes and their pathway information; information on repurposed drugs against various targets of SARS-CoV-2, mutational variants, polypharmacology for COVID-19, drug-drug interaction information, Protein-Protein Interaction (PPI), host protein information, epidemiology, and inhibitors databases. After improvements to the technical description of the platform, testing helped demonstrate the potential to drive open-source computational drug discovery with the platform.

     This evaluation refers to version 1 of the preprint

   Reviewed by GigaByte

5. Molecular Property Diagnostic Suite for COVID-19 (MPDS ^COVID-19 ): An open access disease specific drug discovery portal

   This article has 19 authors:

   1. Lipsa Priyadarsinee
   2. Esther Jamir
   3. Selvaraman Nagamani
   4. Hridoy Jyoti Mahanta
   5. Nandan Kumar
   6. Lijo John
   7. Himakshi Sarma
   8. Asheesh Kumar
   9. Anamika Singh Gaur
   10. Rosaleen Sahoo
   11. S. Vaikundamani
   12. N. Arul Murugan
   13. U. Deva Priyakumar
   14. G.P.S. Raghava
   15. Prasad V. Bharatam
   16. Ramakrishnan Parthasarathi
   17. V. Subramanian
   18. G. Madhavi Sastry
   19. G. Narahari Sastry

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment:

     MPDSCOVID-19 has been developed as a one-stop solution for drug discovery research for COVID-19, running on the Molecular Property Diagnostic Suite (MPDS) platform. This is built upon the open-source Galaxy workflow system, integrating many modules and data specific to COVID-19. Data integrated includes SARS-CoV-2 targets, genes and their pathway information; information on repurposed drugs against various targets of SARS-CoV-2, mutational variants, polypharmacology for COVID-19, drug-drug interaction information, Protein-Protein Interaction (PPI), host protein information, epidemiology, and inhibitors databases. After improvements to the technical description of the platform, testing helped demonstrate the potential to drive open-source computational drug discovery with the platform.

     This evaluation refers to version 1 of the preprint

   Reviewed by GigaByte

6. An improved chromosome-level genome assembly of perennial ryegrass ( Lolium perenne L.)

   This article has 7 authors:

   1. Yutang Chen
   2. Roland Kölliker
   3. Martin Mascher
   4. Dario Copetti
   5. Axel Himmelbach
   6. Nils Stein
   7. Bruno Studer

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment:

     This Data Release paper presents an updated genome assembly of the doubled haploid perennial ryegrass (Lolium perenne L.) genotype Kyuss (Kyuss v2.0). To correct for structural the authors de novo assembled the genome again with ONT long-reads and generated 50-fold coverage high-throughput chromosome conformation capture (Hi-C) data to assist pseudo-chromosome construction. After being asked for some more improvements to gene and repeat annotation the authors now demonstrate the new assembly is more contiguous, more complete, and more accurate than Kyuss v1.0 and shows the correct pseudo-chromosome structure. This more accurate data have great potential for downstream genomic applications, such as read mapping, variant calling, genome-wide association studies, comparative genomics, and evolutionary biology. These future analyses being able to benefit forage and turf grass research and breeding.

     This evaluation refers to version 1 of the preprint

   Reviewed by GigaByte

7. Citizen science data on urban forageable plants: a case study in Brazil

   This article has 21 authors:

   1. Filipi Miranda Soares
   2. Luís Ferreira Pires
   3. Maria Carolina Garcia
   4. Lidio Coradin
   5. Natalia Pirani Ghilardi-Lopes
   6. Rubens Rangel Silva
   7. Aline Martins de Carvalho
   8. Anand Gavai
   9. Yamine Bouzembrak
   10. Benildes Coura Moreira dos Santos Maculan
   11. Sheina Koffler
   12. Uiara Bandineli Montedo
   13. Debora Pignatari Drucker
   14. Raquel Santiago
   15. Maria Clara Peres de Carvalho
   16. Ana Carolina da Silva Lima
   17. Hillary Dandara Elias Gabriel
   18. Stephanie Gabriele Mendonça de França
   19. Karoline Reis de Almeida
   20. Bárbara Junqueira dos Santos
   21. Antonio Mauro Saraiva

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment:

     This is a Data Release paper describing data sets derived from the Pomar Urbano project cataloging edible fruit-bearing plants in Brazil. Including data sourced from the citizen science iNaturalist app, tracking the distribution and monitoring of these plants within urban landscapes (Brazilian state capitals). The data was audited and peer reviewed and put into better context, and there is a companion commentary in GigaScience journal better explaining the rationale for the study. Demonstrating this data providing a platform for understanding the diversity of fruit-bearing plants in select Brazilian cities and contributing to many open research questions in the existing literature on urban foraging and ecosystem services in urban environments.

     This evaluation refers to version 1 of the preprint

   Reviewed by GigaByte

8. SAW: an efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics

   This article has 17 authors:

   1. Chun Gong
   2. Shengkang Li
   3. Leying Wang
   4. Fuxiang Zhao
   5. Shuangsang Fang
   6. Dong Yuan
   7. Zijian Zhao
   8. Qiqi He
   9. Mei Li
   10. Weiqing Liu
   11. Zhaoxun Li
   12. Hongqing Xie
   13. Sha Liao
   14. Ao Chen
   15. Yong Zhang
   16. Yuxiang Li
   17. Xun Xu

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment:

     One limiting factor in the adoption of spatial omics research are workflow systems for data preprocessing, and to address these authors developed the SAW tool to process Stereo-seq data. The analysis steps of spatial transcriptomics involve obtaining gene expression information from space and cells. Existing tools face issues with large data sets, such as intensive spatial localization, RNA alignment, and excessive memory usage. These issues affect the process's applicability and efficiency. To address this, this paper presents a high-performance open-source workflow called SAW for Stereo-Seq. This includes mRNA position reconstruction, genome alignment, matrix generation, clustering, and result file generation for personalized analysis. During review the authors have added examples of MID correction in the article to make the process easier to understand. And In the future, more accurate algorithms or deep learning models may further improve the accuracy of this pipeline.

     *This evaluation refers to version 1 of the preprint

   Reviewed by GigaByte

9. Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images

   This article has 14 authors:

   1. Bohan Zhang
   2. Mei Li
   3. Qiang Kang
   4. Zhonghan Deng
   5. Hua Qin
   6. Kui Su
   7. Xiuwen Feng
   8. Lichuan Chen
   9. Huanlin Liu
   10. Shuangsang Fang
   11. Yong Zhang
   12. Yuxiang Li
   13. Susanne Brix
   14. Xun Xu

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment:

     This paper describes a new spatial transcriptomics method that that utilizes cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. STCellbin is an update of StereoCell, now using a more advanced cell segmentation technique, so more accurate cell boundaries can be obtained, allowing more reliable single-cell spatial gene expression profiles to be obtained. After peer review more comparisons were added and more description given on what was upgraded in this version to convince the reviewers. Demonstrating it is a more reliable method, particularly for analyzing high-resolution and large-field-of-view spatial transcriptomic data. And extending the capability to automatically process Stereo-seq cell membrane/wall staining images for identifying cell boundaries.

     This evaluation refers to version 2 of the preprint

   Reviewed by GigaByte

10. BatchEval Pipeline: batch effect evaluation workflow for multiple datasets joint analysis

    This article has 6 authors:

    1. Chao Zhang
    2. Qiang Kang
    3. Mei Li
    4. Hongqing Xie
    5. Shuangsang Fang
    6. Xun Xu

    This article has been curated by 1 group:

    • Curated by GigaByte

      Editors Assessment:

      For better data quality assessment of large spatial transcriptomics datasets this new BatchEval software has been developed as a batch effect evaluation tool. This generates a comprehensive report with assessment findings, including basic information of integrated datasets, a batch effect score, and recommended methods for removing batch effects. The report also includes evaluation details for the raw dataset and results from batch effect removal methods. Through peer review and clarification of a number of points it now looks convincing that this tool helps researchers identify and remove batch effects, ensuring reliable and meaningful insights from integrated datasets. Potentially making the tool valuable for researchers who need to analyze large datasets of this type, as it provides an easy and reliable way to assess data quality and ensures that downstream analyses are robust and reliable.

      This evaluation refers to version 1 of the preprint

    Reviewed by GigaByte