Bioinformatics

Identification of Potential Hub Genes and Therapeutic Targets in Colorectal Cancer Using Integrated Bioinformatics Approaches

1. Kamalakannan D
2. Manivannan R
3. Suresh Gopal Kumar
4. Dilip Kumar

Reviewed by PREreview

CompactTree: a lightweight header-only C++ library and Python wrapper for ultra-large phylogenetics

1. Niema Moshiri

• Curated by GigaByte

  Editors Assessment:

  As volumes of viral and bacterial sequence data grow exponentially, the field of computational phylogenetics now demands resources to manage the burgeoning scale of this input data. This study introduces CompactTree, a C++ library designed for ultra-large phylogenetic trees with millions of tips. To address these scalability issues while maintaining ease of incorporation into external code bases, CompactTree is a header-only library with enhanced performance utilizing minimal dependencies, optimized node representation, and memory-efficient tree structure schemes. Resulting in significantly reduced memory footprints and improved processing times. Peer review requested some more detail on the functionality and some real-world examples, demonstrating the current utility of the tool. Although primarily supporting the (text-based) Newick format, the increased and extensibility scalability holds promise for multiple biological and epidemiological applications supporting more complex formats such as Nexus and NeXML. The tool is open source (GPLv3 licensed) and available in GitHub: https://niema.net/CompactTree

  This evaluation refers to version 1 of the preprint

Reviewed by GigaByte

SqueezeCall: nanopore basecalling using a Squeezeformer network

1. Zhongxu Zhu

• Curated by GigaByte

  Editors Assessment:

  The accuracy of basecalling of nanopore sequencing still needs to be improved. With recent advances in deep learning this paper introduces SqueezeCall, a novel end-to-end tool for accurate basecalling. This uses Squeezeformer-achitecture which integrates local context extraction through convolutional layers and long-range dependency modeling via global context acquisition. Testing and peer review demonstrated that SqueezeCall outperformed traditional RNN and Transformer-based basecallers across multiple datasets, indicating its potential to refine genomic assembly and facilitate direct detection of modified bases in future genomic analytics. Future work is ongoing that will focus on training on highly curated datasets, including known modifications, to further increase research value. SqueezeCall is MIT licensed and available from GitHub here: https://github.com/labcbb/SqueezeCall

  This evaluation refers to version 1 of the preprint

Reviewed by GigaByte

Decoding the Molecular Language of Proteins with Evolla

1. Xibin Zhou
2. Chenchen Han
3. Yingqi Zhang
4. Huan Du
5. Jiayuan Tian
6. Jin Su
7. Renju Liu
8. Kai Zhuang
9. Shiyu Jiang
10. Anthony Gitter
11. Li Liu
12. Huayu Li
13. Meiqi Wu
14. Shiyang You
15. Zichen Yuan
16. Feng Ju
17. Huilin Zhang
18. Wei Zheng
19. Fengyuan Dai
20. Yuyang Zhou
21. Yuyang Tao
22. Dan Wu
23. Zongze Shao
24. Yang Liu
25. Hongyuan Lu
26. Fajie Yuan

Reviewed by preLights

CryoNeRF: reconstruction of homogeneous and heterogeneous cryo-EM structures using neural radiance field

1. Huaizhi Qu
2. Xiao Wang
3. Yuanyuan Zhang
4. Sheng Wang
5. William Stafford Noble
6. Tianlong Chen

Reviewed by PREreview

Discovery of Novel Inhibitors of HMG-CoA Reductase using Bioactive Compounds isolated from Cochlospermum Species through Computational Methods

1. Toba Isaac Olatoye

Reviewed by PREreview

The inflammatory skin disease map (ISD map): an interactive computational resource focused on psoriasis and atopic dermatitis molecular mechanisms

1. Marcio L. Acencio
2. Oxana Lopata
3. Ahmed A. Hemedan
4. Nick Dand
5. Stephan Weidinger
6. Lavinia Paternoster
7. Sara J. Brown
8. Joseph Rastrick
9. Catherine H. Smith
10. Matthias Hübenthal
11. Ravi Ramessur
12. Matladi Ndlovu
13. Soumyabrata Ghosh
14. Xinhui Wang
15. Reinhard Schneider
16. Venkata Satagopam
17. Marek Ostaszewski

Reviewed by Arcadia Science

Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learning

1. Ruihan Dong
2. Rongrong Liu
3. Ziyu Liu
4. Yangang Liu
5. Gaomei Zhao
6. Honglei Li
7. Shiyuan Hou
8. Xiaohan Ma
9. Huarui Kang
10. Jing Liu
11. Fei Guo
12. Ping Zhao
13. Junping Wang
14. Cheng Wang
15. Xingan Wu
16. Sheng Ye
17. Cheng Zhu

• Curated by eLife

  eLife Assessment

  This study presents a useful pipeline for de novo design of antimicrobial peptides active both against bacteria and viruses. The method is based on deep learning, using a GAN generator and a regression tasked to predict antimicrobial activity. The experimental evidence supporting the conclusions is solid, with 24 validated peptides, although some additional justifications of the computational strategy would be a plus. This work will be of interest to the community working on machine learning for biomedical applications and specifically on antimicrobial peptides.

Reviewed by eLife

xRead: a coverage-guided approach for scalable construction of read overlapping graph

1. Tangchao Kong
2. Yadong Wang
3. Bo Liu

Reviewed by GigaScience

TransHLA: A Hybrid Transformer Model for HLA-Presented Epitope Detection

1. Tianchi Lu
2. Xueying Wang
3. Wan Nie
4. Miaozhe Huo
5. Shuaicheng Li

Reviewed by GigaScience