Bioinformatics

Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

1. Michal R. Grzadkowski
2. Hannah Manning
3. Julia Somers
4. Emek Demir

Reviewed by eLife

The risks of using the chi-square periodogram to estimate the period of biological rhythms

1. Michael C. Tackenberg
2. Jacob J. Hughey

Reviewed by Review Commons

Mapping the Algal Secret Genome: The small RNA Locus Map for Chlamydomonas reinhardtii

1. Sebastian Y. Müller
2. Nicholas E. Matthews
3. Adrian A. Valli
4. David C. Baulcombe

Reviewed by Review Commons

CytofRUV: Removing unwanted variation to integrate multiple CyTOF datasets

1. Marie Trussart
2. Charis E Teh
3. Tania Tan
4. Lawrence Leong
5. Daniel HD Gray
6. Terence P. Speed

Reviewed by eLife

Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection

1. Müşerref Duygu Saçar Demirci
2. Aysun Adan

Reviewed by ScreenIT, PeerJ

Identification of novel mutations in RNA-dependent RNA polymerases of SARS-CoV-2 and their implications on its protein structure

1. Gyanendra Bahadur Chand
2. Atanu Banerjee
3. Gajendra Kumar Azad

Reviewed by PeerJ, ScreenIT

A spatially explicit and mechanistic model for exploring coral reef dynamics

1. Bruno S. Carturan
2. Jean-Philippe Maréchal
3. Corey J. A. Bradshaw
4. Jason Pither
5. Lael Parrott

Reviewed by eLife

VolcaNoseR – a web app for creating, exploring, labeling and sharing volcano plots

1. Joachim Goedhart
2. Martijn S. Luijsterburg

Reviewed by eLife

A fully automated deep learning pipeline for high-throughput colony segmentation and classification

1. Sarah H. Carl
2. Lea Duempelmann
3. Yukiko Shimada
4. Marc Bühler

Reviewed by Review Commons

Analysis of RNA processing directly from spatial transcriptomics data reveals previously unknown regulation

1. Julia Olivieri
2. Julia Salzman

• Curated by eLife

  eLife assessment:

  This important study describes spatial RNA processing events by combining methods for single-cell transcriptomics data with spatial transcriptomics data. The evidence supporting the claims of the authors is solid, although the analysis could be further strengthened by including a broader range of samples as well as orthogonal validation either by experimental methods or simulated data. The work will be of general interest to researchers in the spatial transcriptomics field as well as researchers investigating alternative pre-mRNA processing across diverse tissues.

Reviewed by eLife