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Latest preprint reviews

1. Poly pipeline: a polyvalent spatial transcriptomics workflow validated across polyploid and diploid organisms

   This article has 3 authors:

   1. Pedro C. Carvalho
   2. Tori MIllsteed
   3. Robert J. Henry

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment:

     This Technical Release (software/workflow paper) presents Poly Pipeline, an open-source spatial transcriptomics workflow designed to handle the data sparsity, fragmented proprietary formats and duplicated-gene complexity that make polyploid plant genomes difficult to analyze. Carvalho et al. combine a converter that turns h5ad and GEM files into the GEF format used by Stereopy, quality filtering, Louvain, Leiden and Spatial Leiden clustering, a user-defined “anchoring” step for assigning regions, and highly variable and marker gene detection. Optional hdWGCNA co-expression networks are built on KNN-derived metacells, intended to retain low-expressed duplicated genes that would otherwise be discarded as dropouts. The pipeline was developed on a hexaploid wheat seed Stereo-seq dataset and benchmarked on rice, Arabidopsis and mouse data in different formats. Spatial Leiden gave more cohesive tissue domains than standard Leiden (15 versus 26 clusters in wheat), and the largest dataset (40,248 cells, 70,766 genes) ran in about 23.5 minutes using a peak of 31.8 GB of RAM. The benchmarking mainly shows that the workflow runs reliably and produces spatially coherent domains across organisms and formats. It does not directly test the claim that hdWGCNA better preserves duplicated-gene signatures, so that remains largely supported by argument rather than comparison with other methods. Nonetheless, with the code available on GitHub under an MIT license (with Zenodo, WorkflowHub, RRID and bio.tools entries), this provides a reproducible, HPC-ready framework that should help plant researchers working with complex genomes and multi-platform spatial datasets.

     This evaluation refers to version 1 of the preprint

   Reviewed by GigaByte

2. Single-cell RNA sequencing data processing using cloud-based serverless computing

   This article has 5 authors:

   1. Ling-Hong Hung
   2. Niharika Nasam
   3. Chris Biju
   4. Wes Lloyd
   5. Ka Yee Yeung

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment:

     This Technical Release (software) presents a serverless cloud workflow for the computationally intensive alignment step of single-cell RNA-seq processing, extending the authors’ earlier serverless approach for bulk RNA-seq. Hung et al. built on the Piscem-Alevin-Fry pipeline and implemented it with AWS Lambda functions, working around their memory, storage and runtime limits by splitting large FASTQ files into four-million-read shards, streaming inputs from S3 straight into the aligner, and merging shard outputs as they complete. Because every stage overlaps, alignment adds only seconds beyond the cost of splitting and uploading. Compared with an optimized 32-thread on-server run on identical EC2 hardware, the serverless workflow was faster for all three datasets tested (two 10x Genomics PBMC datasets and a 450 GB, 13-sample MorPhiC pancreatic differentiation knockout dataset). The speedup grew with dataset size, from 1.27× to 1.93× to 7.01× for the largest, cutting about 2.4 hours of alignment to under 21 minutes, with outputs validated as identical to the conventional pipeline. The authors are transparent that this speed comes at a cost: the serverless runs were more expensive at list prices, though the premium shrank to about 13% for the largest dataset, and gains for small datasets are limited by driver-side preprocessing. With open-source code under an MIT license, an automated orchestration script, and deposited reference index and count matrices, this is a useful and reproducible template for processing large single-cell datasets, and for applying serverless computing to other bioinformatics workflows.

     This evaluation refers to version 1 of the preprint

   Reviewed by GigaByte

3. Bacterial metagenome in plaque, saliva, and tumor samples from individuals with and without OSCC by next-generation sequencing

   This article has 9 authors:

   1. Alveiro Erira
   2. Dabeiba Adriana García Robayo
   3. Fredy Omar Gamboa
   4. Andrés Ignacio Chala Galindo
   5. Andrey Moreno Torres
   6. Angel Cid Arregui
   7. Eliana Muñoz
   8. José Fernando Noguera Polania
   9. Fabian Tobar Tosse

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment:

     This Data Release presents shotgun metagenomic sequencing data from the oral microbiome of patients with oral squamous cell carcinoma (OSCC), a setting where most previous studies have used 16S rRNA amplicon sequencing with limited species-level resolution. The authors sequenced dental plaque, saliva and tumor tissue from 10 OSCC patients, alongside plaque and saliva from 10 healthy controls, on the Illumina MiSeq platform. After three control saliva samples failed quality control, 46 samples remained, with roughly 75,000-100,000 reads per sample after filtering. Reads were classified with Kraken2 and Bracken, and the sequencing data are deposited in NCBI, with supporting data in GigaDB and a Conda-based pipeline on GitHub. The authors report that dental plaque had the highest diversity, that saliva was more homogeneous, and that tumor tissue had a distinct profile dominated by taxa such as Sellimonas catena, Escherichia coli and Lacrimispora sp. HJ-01. The plaque of OSCC patients was enriched in Pyramidobacter piscolens and Parvimonas parva, compared with Streptococcus koreensis in controls. The authors are open about the limitations of this small, convenience-sampled exploratory cohort: oral hygiene differs markedly between patients and controls, 90% of participants are female, and no negative extraction controls or mock communities were included. The taxonomic differences should therefore be read as hypothesis-generating rather than disease-specific. Even so, the matched multi-niche sampling, open data and reproducible workflow make this a useful resource for integration with other metagenomic datasets and for follow-up functional studies of the oral microbiome in OSCC.

     This evaluation refers to version 1 of the preprint

   Reviewed by GigaByte

4. A chromosome-level reference genome for Pacific herring (Clupea pallasii) from the Bering Sea

   This article has 5 authors:

   1. Laura E. Timm
   2. Yin-Chen Hsieh
   3. J. Andrés López
   4. Sydney A. Almgren
   5. Jessica R. Glass

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment:

     Here is a Data Release article sharing the reference genome of the Pacific herring (Clupea pallasii), a species with broad ecological, economic, and cultural importance. This specimen coming from the eastern Bering Sea which are genetically quite different from those from the Gulf of Alaska. This high-quality chromosome-scale reference was built from Hi-C and PacBio Hifi reads, creating a 795MB assembly anchoring to 26 chromosomes. There was initially a mixup with the data availability but after review this was resolved and all data is publicly available. This data allows study of cryptic diversity in Clupea and its evolutionary history in the subarctic.

     This evaluation refers to version 1 of the preprint

   Reviewed by GigaByte

5. Closely related, yet phenotypically different - Genome assemblies of two sister species of widow spiders: Latrodectus hasselti and L. katipo, Theridiidae

   This article has 8 authors:

   1. Vladislav Ivanov
   2. Kardelen Özgün Uludağ
   3. Jutta M. Schneider
   4. Yannis Schöneberg
   5. Susan Kennedy
   6. Alexander Ben Hamadou
   7. Cor J. Vink
   8. Henrik Krehenwinkel

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment:

     The paper presents high-quality reference genome assemblies for two closely related widow spider species, the Australian redback (Latrodectus hasselti) and the New Zealand endemic katipō (L. katipo), providing an important genomic resource for studies of venom evolution, adaptation, and conservation biology. Rather than simply reporting assembly statistics, the authors compare the genomic architecture of the two species, showing that both genomes are highly complete (BUSCO scores >94%) and share similar repeat content, while differing in overall genome size, scaffold continuity, and predicted gene numbers. The chromosome-level assembly achieved for L. katipo enables more detailed comparative analyses than previously possible, whereas the more fragmented L. hasselti assembly still captures a highly complete gene repertoire. Ortholog analyses reveal substantial conservation between the species but also indicate lineage-specific differences, with evidence suggesting either gene duplication in L. hasselti or gene loss in L. katipo, providing insight into their divergent evolutionary histories despite their close relationship and contrasting ecological distributions. Beyond generating sequence data, the study establishes foundational genomic resources that will facilitate future investigations into venom gene evolution, population genomics, species divergence, and the conservation of these ecologically and medically significant spiders.

     This evaluation refers to version 1 of the preprint

   Reviewed by GigaByte

6. AI in practice: a multilingual survey of 2025 BioHackathon participants

   This article has 9 authors:

   1. Nattawet Sriwichai
   2. Lucas Feriau
   3. Pumipat Tongyoo
   4. Yukiko Noda
   5. Hikaru Gyoji
   6. Pitiporn Noisagul
   7. Susumu Goto
   8. David Steinberg
   9. Chatarin Wangsanuwat

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment:

     This Data Release presents a multilingual dataset collected from a survey of participants and community members at the 2025 DBCLS BioHackathon, with the goal of understanding how AIis being used in bioinformatics and related scientific fields. The survey, available in English, Japanese, and Thai, gathered responses from 105 participants about AI usage frequency, applications, challenges, institutional support, satisfaction, concerns, and demographic information. The released dataset includes anonymized raw responses, a cleaned English-language version for quantitative analysis, the original questionnaire, a data dictionary, and a translation lookup table to support reproducible research. To protect participant privacy, the authors carefully removed personally identifiable information. This openly available resource provides researchers with valuable data for studying AI adoption, evaluating policy and institutional support, and developing methods for analyzing survey data related to AI use in scientific research. Overall, this work contributes a well-documented, reusable dataset that can help inform future research on the evolving role of artificial intelligence in genomics, bioinformatics, software development, and the broader scientific community.

     This evaluation refers to version 1 of the preprint

   Reviewed by GigaByte

7. Transcriptomic profile of embryoid bodies under hypoxia at single cell level

   This article has 5 authors:

   1. Bárbara Acosta-Iborra
   2. Yosra Berrouayel
   3. Laura Puente-Santamaría
   4. Luis del Peso
   5. Benilde Jiménez

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment:

     This is a Data Release paper describing a mouse embryoid body single-cell RNA-seq dataset generated to study how oxygen availability shapes early cell differentiation. Acosta-Iborra et al. differentiated R1 mouse embryonic stem cells into embryoid bodies for 8 or 10 days, exposing them to hypoxia or normoxia for the final 16 or 48 hours of differentiation, then profiled thousands of cells per condition using droplet-based scRNA-seq from 10X. This yielding eight raw/filtered HDF5 count matrices across the four conditions. This was validated with flow cytometry, immunofluorescence, and EdU assays, confirming that hypoxia increased endothelial marker expression and vascular network complexity while inducing cell cycle arrest. This pattern mirrored transcriptionally, with hypoxic samples showing markedly higher proportions of cells in G0/G1 phase and elevated hypoxia gene-signature scores. QC analysis (and peer review in GigaByte) confirmed high data quality across samples, and conservative low-resolution clustering revealed a largely homogeneous progenitor population with a smaller, more differentiated subset. While there are limitations (mature endothelial cells were too sparse to robustly test the original hypothesis) the authors present this as an open, well-validated resource for comparative studies of hypoxia responses, benchmarking single-cell computational tools, and investigating early lineage specification and oxygen signaling more broadly.

     This evaluation refers to version 1 of the preprint

   Reviewed by GigaByte

8. The genome of the reef-building coral Porites harrisoni from the southern Persian/Arabian Gulf

   This article has 7 authors:

   1. Anna Fiesinger
   2. Abdoallah Sharaf
   3. Rachel Alderdice
   4. Gabriela Perna
   5. Hannah Manns
   6. John A. Burt
   7. Christian R. Voolstra

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment:

     This data paper is a genome note presenting the assembly of Porites harrisoni, a stony coral species endemic to the thermally extreme southern Persian Gulf. Using ONT PromethION long nanopore reads the final genome size encompassed 626.7 Mb across 1,883 contigs, achieving a BUSCO completeness of 86.3%. This revealed significant repeat content, comprising 59.23% of the nuclear genome and highlighting a diploid structure with predominant homozygosity. A total of 27,823 protein-coding genes were annotated from this assembly, facilitating discussions on thermal resilience under climate change. The research underscores the genomic framework supporting adaptive capacities in corals, with implications for evolutionary biology and conservation science, especially in context to ongoing ocean warming.

     This evaluation refers to version 1 of the preprint

   Reviewed by GigaByte

9. Inference of admixture in dogs from whole genome sequences

   This article has 6 authors:

   1. Gregory Kislik
   2. Garrett Moore
   3. Liudmilla Rubbi
   4. Veninka Nikki Supara
   5. Grace Chen
   6. Matteo Pellegrini

   This article has been curated by 1 group:

   • Curated by GigaByte

     Editors Assessment:

     In this new methodological work researchers investigate the genetic structure and admixture patterns among dog breeds through a comprehensive analysis using whole genome sequencing data. A reference population was established comprising 349 individuals across 65 breeds, from which breed-informative single nucleotide polymorphisms (SNPs) were derived. Using the SCOPE algorithm previously employed in many global ancestry studies to estimate admixture proportions effectively, this demonstrated strong accuracy even at low sequencing depths (<1x). After peer review suggested changes to data processing the work was suitably solid to make some interesting findings using this approach. Results indicate that specific breeds, such as Catahoula Leopard Dogs and Greek Tracers, present unique challenges in admixture inference due to their genetic proximity to other breeds. With challenges in estimating Pit Bull Terrier ancestry/admixture, suggesting that there could be several genotypes associated with the Pit Bull Terrier breed . The methods provide a robust framework for future assessments of canine genetic diversity and health implications in canid populations. And processed reference population data is also available in the Github repository for reuse.

     This evaluation refers to version 1 of the preprint

   Reviewed by GigaByte

10. EMImR: a Shiny application for identifying transcriptomic and epigenomic changes

    This article has 5 authors:

    1. Hiba Ben Aribi
    2. Careen Naitore
    3. Farah Ayadi
    4. Souheila Guerbouj
    5. Olaitan I. Awe

    This article has been curated by 1 group:

    • Curated by GigaByte

      Editors Assessment:

      Coded and written up as part of the African Society for Bioinformatics and Computational Biology (ASBCB) Omicscodeathons, EMImR is a novel Shiny application for transcriptomic and epigenomic change identification and correlation wrapped up using a combination of Bioconductor and CRAN packages. Case studies are on publicly available GEO data corresponding to sequencing data of human blood cell samples of multiple sclerosis patients to demonstrate how the tool works. And a documentation and videos are provided. Peer review and the study highlighting the usefulness of the developed tool for analyzing transcriptomic and epigenomic data.

      This evaluation refers to version 1 of the preprint

    Reviewed by GigaByte