Bioinformatics

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 denormalisation tool for integrating single-cell transcriptomics data

1. Yin Huang
2. Anna Vathrakokili Pournara
3. Ying Ao
4. Lirong Yang
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

Label-free biochemical imaging and timepoint analysis of neural organoids via deep learning-enhanced Raman microspectroscopy

1. Dimitar Georgiev
2. Ruoxiao Xie
3. Daniel Reumann
4. Xiaoyu Zhao
5. Álvaro Fernández-Galiana
6. Mauricio Barahona
7. Molly M. Stevens

Reviewed by Arcadia Science

Leveraging AI to explore structural contexts of post-translational modifications in drug binding

1. Kirill E. Medvedev
2. R. Dustin Schaeffer
3. Nick V. Grishin

Reviewed by eLife

Comparative analysis of eccDNA and circRNA tools shows increased accuracy of tool combination

1. Aitor Zabala
2. Alex M. Ascensión
3. Iñigo Prada-Luengo
4. David Otaegui

Reviewed by GigaScience