Bioinformatics

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

Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification

1. Chakravarthi Kanduri
2. Milena Pavlović
3. Lonneke Scheffer
4. Keshav Motwani
5. Maria Chernigovskaya
6. Victor Greiff
7. Geir K Sandve

Reviewed by GigaScience

CRISPR-Analytics (CRISPR-A): A platform for precise analytics and simulations for gene editing

1. Marta Sanvicente-García
2. Albert García-Valiente
3. Socayna Jouide
4. Jessica Jaraba-Wallace
5. Eric Bautista
6. Marc Escobosa
7. Avencia Sánchez-Mejías
8. Marc Güell

Reviewed by Review Commons

Network-based multi-omics integration reveals metabolic at-risk profile within treated HIV-infection

1. Flora Mikaeloff
2. Marco Gelpi
3. Rui Benfeitas
4. Andreas D Knudsen
5. Beate Vestad
6. Julie Høgh
7. Johannes R Hov
8. Thomas Benfield
9. Daniel Murray
10. Christian G Giske
11. Adil Mardinoglu
12. Marius Trøseid
13. Susanne D Nielsen
14. Ujjwal Neogi

• Curated by eLife

  eLife assessment

  This study systematically integrates multi-omics data to identify the metabolic at-risk profiles within people living with HIV on antiretroviral therapy and presents findings that have focused importance and scope. The methods, data, and analyses as described now only partially support the primary claims.

Reviewed by eLife

Evaluation of genetic demultiplexing of single-cell sequencing data from model species

1. Joseph F Cardiello
2. Alberto Joven Araus
3. Sarantis Giatrellis
4. Clement Helsens
5. András Simon
6. Nicholas D Leigh

Reviewed by Review Commons