Bioinformatics

A novel computational pipeline for var gene expression augments the discovery of changes in the Plasmodium falciparum transcriptome during transition from in vivo to short-term in vitro culture

1. Clare Andradi-Brown
2. Jan Stephan Wichers-Misterek
3. Heidrun von Thien
4. Yannick D Höppner
5. Judith AM Scholz
6. Helle Hansson
7. Emma Filtenborg Hocke
8. Tim Wolf Gilberger
9. Michael F Duffy
10. Thomas Lavstsen
11. Jake Baum
12. Thomas D Otto
13. Aubrey J Cunnington
14. Anna Bachmann

• Curated by eLife

  eLife assessment

  Focusing mainly on var genes, the investigators performed comprehensive computational analyses of gene expression in malaria parasites isolated from patients and assessed changes that occur as these parasites adapt to in vitro culture conditions. The study provides an improved computational pipeline for monitoring var gene expression, and importantly, the study documents changes in expression of the core genome and thus provides insights into metabolic adaptations that parasites undergo while transitioning to culture conditions. The findings are important for their technical advances that are more rigorous than the current state-of-the-art. The solid data analyses, broadly support the claims with only minor weaknesses, tell us to be cautious when interpreting results obtained only from cultured parasites.

Reviewed by eLife

Detection of new pioneer transcription factors as cell-type-specific nucleosome binders

1. Yunhui Peng
2. Wei Song
3. Vladimir B Teif
4. Ivan Ovcharenko
5. David Landsman
6. Anna R Panchenko

• Curated by eLife

  eLife assessment

  This valuable study aims to identify pioneer transcription factors, which are defined as transcription factors that compete with nucleosomes for DNA binding. The authors provide methods for identifying pioneer transcription factors on a cell type basis, using nucleosome positioning and motif information across different cell lines. The evidence to support the claims is largely solid. This work will be of interest to computational and molecular biologists working on transcription factors.

Reviewed by eLife

Integrated single-dose kinome profiling data is predictive of cancer cell line sensitivity to kinase inhibitors

1. Chinmaya U. Joisa
2. Kevin A. Chen
3. Matthew E. Berginski
4. Brian T. Golitz
5. Madison R. Jenner
6. Gabriela Herrera Loeza
7. Jen Jen Yeh
8. Shawn M. Gomez

Reviewed by PeerJ

Integrated genomics provides insights for the evolution of the polyphosphate accumulation trait of Ca. Accumulibacter

1. Xiaojing Xie
2. Xuhan Deng
3. Liping Chen
4. Jing Yuan
5. Hang Chen
6. Chaohai Wei
7. Xianghui Liu
8. Stefan Wuertz
9. Guanglei Qiu

Reviewed by Arcadia Science

Reprogramming of cardiac phosphoproteome, proteome, and transcriptome confers resilience to chronic adenylyl cyclase-driven stress

1. Jia-Hua Qu
2. Khalid Chakir
3. Kirill V Tarasov
4. Daniel R Riordon
5. Maria Grazia Perino
6. Allwin Jennifa Silvester
7. Edward G Lakatta

• Curated by eLife

  eLife assessment

  This study describes important results from cardiac-specific overexpression of adenylyl cyclase type 8 (TGAC8) mice that was integrated with transcriptomic and proteomic evidence. The paper convincingly provides new insights into how one can interpret signals from visceral organs.

Reviewed by eLife

cellsnake: a user-friendly tool for single-cell RNA sequencing analysis

1. Sinan U. Umu
2. Karoline Rapp Vander-Elst
3. Victoria T. Karlsen
4. Manto Chouliara
5. Espen Sønderaal Bækkevold
6. Frode Lars Jahnsen
7. Diana Domanska

Reviewed by GigaScience

SpheroScan: A User-Friendly Deep Learning Tool for Spheroid Image Analysis

1. Akshay Akshay
2. Mitali Katoch
3. Masoud Abedi
4. Mustafa Besic
5. Navid Shekarchizadeh
6. Fiona C. Burkhard
7. Alex Bigger-Allen
8. Rosalyn M. Adam
9. Katia Monastyrskaya
10. Ali Hashemi Gheinani

Reviewed by GigaScience

Developing best practices for genotyping-by-sequencing analysis in the construction of linkage maps

1. Cristiane Hayumi Taniguti
2. Lucas Mitsuo Taniguti
3. Rodrigo Rampazo Amadeu
4. Jeekin Lau
5. Gabriel de Siqueira Gesteira
6. Thiago de Paula Oliveira
7. Getulio Caixeta Ferreira
8. Guilherme da Silva Pereira
9. David Byrne
10. Marcelo Mollinari
11. Oscar Riera-Lizarazu
12. Antonio Augusto Franco Garcia

Reviewed by GigaScience

Computational prediction of human deep intronic variation

1. Pedro Barbosa
2. Rosina Savisaar
3. Maria Carmo-Fonseca
4. Alcides Fonseca

Reviewed by GigaScience

Charting cellular differentiation trajectories with Ricci flow

1. Anthony Baptista
2. Ben D. MacArthur
3. Christopher R. S. Banerji

Reviewed by Review Commons