Bioinformatics

Calibrated analysis framework for nanopore direct RNA sequencing uncovers cell-specific m6A stoichiometry at conserved sites

1. Denise Ohnezeit
2. Elene Loliashvili
3. Gregory Putzel
4. Ruth Verstraten
5. Jianheng Liu
6. Luke S Nicholson
7. Alejandro Pironti
8. Samie R Jaffrey
9. Daniel P Depledge
10. Angus C Wilson

• Curated by eLife

  eLife Assessment

  This valuable study compares orthogonal approaches for detecting RNA chemical modifications and provides a helpful framework for improving the reliability of direct RNA sequencing-based identification of RNA modifications. The evidence supporting the technical benchmarking claims is solid. However, support for the broader biological conclusions is not as strong, and the quantitative interpretation of the results, as well as the limitations of the underlying models, would benefit from further clarification.

Reviewed by eLife

GEfetch2R : fetching single-cell/bulk RNA-seq data from public repositories to R and benchmarking the subsequent format conversion tools

1. Yabing Song
2. Jianbin Wang
3. Jiaxin Gao

Reviewed by GigaScience

HVRLocator: a computationally efficient tool for identifying hypervariable regions in large 16S rRNA datasets

1. Clara Arboleda-Baena
2. Felipe Borim Corrêa
3. João Pedro Saraiva
4. Santiago Castillo-Rivadeneira
5. Jonas Coelho Kasmanas
6. Antonis Chatzinotas
7.  Stephanie D Jurburg

Reviewed by GigaScience

Gene specificity landscapes for comparative transcriptomic analysis across tissues, cell types, and species

1. Erik Bot
2. Jose Davila-Velderrain

• Curated by eLife

  eLife Assessment

  This important study advances a new computational approach to measure and visualize gene expression specificity across different tissues and cell types. The framework is potentially helpful for improving the way gene expression specificity is defined across biological datasets, especially among single-cell datasets. The evidence supporting the method is generally solid, although further evaluation of the method's robustness and comparison to other approaches would strengthen the conclusions.

Reviewed by eLife

MADRe: Strain-level metagenomic classification through assembly-driven database reduction

1. Josipa Lipovac
2. Mile Šikić
3. Riccardo Vicedomini
4. Krešimir Križanović

Reviewed by GigaScience

scDenorm: a denormalization tool for integrating single-cell transcriptomics data

1. Yin Huang
2. Anna Vathrakokoili Pournara
3. Ying Ao
4. Ziliang Huang
5. Hui Zhang
6. Yongjian Zhang
7. Sheng Liu
8. Alvis Brazma
9. Irene Papatheodorou
10. Xinlu Yang
11. Ming Shi
12. Zhichao Miao

Reviewed by GigaScience

Joint analysis of human retroelements-linked histone modification profiles reveals quickly evolving molecular processes connected with cancer

1. Daniil Nikitin

Reviewed by PREreview

Evidence of off-target probe binding affecting 10x Genomics Xenium gene panels compromise accuracy of spatial transcriptomic profiling

1. Caleb Hallinan
2. Hyun Joo Ji
3. Edmund Tsou
4. Steven L Salzberg
5. Jean Fan

• Curated by eLife

  eLife Assessment

  This valuable study identifies and characterizes probe binding errors in a widely used commercial platform for spatial transcriptomics, discovering that at least 21 out of 280 genes in a human breast cancer panel are not accurately detected. The authors provide convincing evidence for their findings through validation against multiple independent sequencing technologies and reference datasets, and they introduce a computational tool to help predict potential off-target probe binding. Given the broad adoption of this platform in biomedical research, this work provides an essential quality control resource that will improve data interpretation across numerous studies.

Reviewed by eLife

The Limitations of TabPFN for High-Dimensional RNA-seq Analysis

1. Summer Zhou
2. Vinayak Agarwal
3. Ashwin Gopinath
4. Timothy Kassis

Reviewed by Arcadia Science

Phylogenetic detection of protein sites associated with continuous traits

1. Louis Duchemin
2. Gerard Muntane Medina
3. Bastien Boussau
4. Philippe Veber

Reviewed by Arcadia Science