Bioinformatics

DeePVP: Identification and classification of phage virion proteins using deep learning

1. Zhencheng Fang
2. Tao Feng
3. Hongwei Zhou
4. Muxuan Chen

Reviewed by GigaScience

Stardust: improving spatial transcriptomics data analysis through space aware modularity optimization based clustering

1. Simone Avesani
2. Eva Viesi
3. Luca Alessandrì
4. Giovanni Motterle
5. Vincenzo Bonnici
6. Marco Beccuti
7. Raffaele Calogero
8. Rosalba Giugno

Reviewed by GigaScience

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data

1. Yunwei Zhang
2. Germaine Wong
3. Graham Mann
4. Samuel Muller
5. Jean Y H Yang

Reviewed by GigaScience

Meta-Prism 2.0: Enabling algorithm for ultra-fast, accurate and memory-efficient search among millions of microbial community samples

1. Kai Kang
2. Hui Chong
3. Kang Ning

Reviewed by GigaScience

Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations

1. M Elise Lauterbur
2. Maria Izabel A Cavassim
3. Ariella L Gladstein
4. Graham Gower
5. Nathaniel S Pope
6. Georgia Tsambos
7. Jeffrey Adrion
8. Saurabh Belsare
9. Arjun Biddanda
10. Victoria Caudill
11. Jean Cury
12. Ignacio Echevarria
13. Benjamin C Haller
14. Ahmed R Hasan
15. Xin Huang
16. Leonardo Nicola Martin Iasi
17. Ekaterina Noskova
18. Jana Obsteter
19. Vitor Antonio Correa Pavinato
20. Alice Pearson
21. David Peede
22. Manolo F Perez
23. Murillo F Rodrigues
24. Chris CR Smith
25. Jeffrey P Spence
26. Anastasia Teterina
27. Silas Tittes
28. Per Unneberg
29. Juan Manuel Vazquez
30. Ryan K Waples
31. Anthony Wilder Wohns
32. Yan Wong
33. Franz Baumdicker
34. Reed A Cartwright
35. Gregor Gorjanc
36. Ryan N Gutenkunst
37. Jerome Kelleher
38. Andrew D Kern
39. Aaron P Ragsdale
40. Peter L Ralph
41. Daniel R Schrider
42. Ilan Gronau

• Curated by eLife

  eLife assessment

  This important paper reports recent improvements and extensions to stdpopsim, a community-driven resource that is built on top of powerful software for performing simulations of population genomic data and provides a catalog of species with curated genomic parameters and demographic models. In addition to describing the new features and species in stdpopsim, the authors provide a set of practical guidelines for implementing realistic simulations. Overall, this convincing manuscript serves as an excellent overview of the utility, challenges, common pitfalls, and best practices of population genomic simulations. It will be of broad interest to population, evolutionary, and ecological geneticists studying humans, model organisms, or non-model organisms.

Reviewed by eLife

A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity

1. Barbara Bravi
2. Andrea Di Gioacchino
3. Jorge Fernandez-de-Cossio-Diaz
4. Aleksandra M Walczak
5. Thierry Mora
6. Simona Cocco
7. Rémi Monasson

• Curated by eLife

  eLife assessment

  In this important work, the authors present a sequence-based approach using transfer learning and Restricted Boltzmann Machines to predict antigen immunogenicity and specificity. The evidence and methodology are compelling. This work should be of interest to immunologists, computational biologists, and biophysicists.

Reviewed by eLife

BrumiR: A toolkit for de novo discovery of microRNAs from sRNA-seq data

1. Carol Moraga
2. Evelyn Sanchez
3. Mariana Galvão Ferrarini
4. Rodrigo A Gutierrez
5. Elena A Vidal
6. Marie-France Sagot

Reviewed by GigaScience

Disease classification for whole blood DNA methylation: meta-analysis, missing values imputation, and XAI

1. Alena Kalyakulina
2. Igor Yusipov
3. Maria Giulia Bacalini
4. Claudio Franceschi
5. Maria Vedunova
6. Mikhail Ivanchenko

Reviewed by GigaScience

Spacemake: processing and analysis of large-scale spatial transcriptomics data

1. Tamas Ryszard Sztanka-Toth
2. Marvin Jens
3. Nikos Karaiskos
4. Nikolaus Rajewsky

Reviewed by GigaScience

Loop detection using Hi-C data with HiCExplorer

1. Joachim Wolff
2. Rolf Backofen
3. Björn Grüning

Reviewed by GigaScience