A how-to guide for code-sharing in biology

Computational biology continues to spread into new fields, becoming more accessible to researchers trained in the wet lab who are eager to take advantage of growing datasets, falling costs, and novel assays that present new opportunities for discovery even outside of the much-discussed developments in artificial intelligence. However, guidance for implementing these techniques is much easier to find than guidance for reporting their use, leaving biologists to guess which details and files are relevant. Here, we provide a set of recommendations for sharing code, with an eye toward guiding those who are comparatively new to applying open science principles to their computational work. Additionally, we review existing literature on the topic, summarize the most common tips, and evaluate the code-sharing policies of the most influential journals in biology, which occasionally encourage code-sharing but seldom require it. Taken together, we provide a user manual for biologists who seek to follow code-sharing best practices but are unsure where to start.Comment: 19 pages, 1 figure; for supporting data see https://doi.org/10.5281/zenodo.1045994

Rxivist.org: Sorting biology preprints using social media and readership metrics.

Preprints have arrived. In increasing numbers, researchers across the life sciences are embracing the once-niche practice, shaking off decades of reluctance and posting hundreds of papers per week to preprint servers, sharing their findings with the community before embarking on the weary march through peer review. However, there are limited methods for individuals sifting through this avalanche of research to identify the preprints that are most relevant to their interests. Here, we describe Rxivist.org, a website that indexes all preprints posted to bioRxiv.org, the largest preprint server in the life sciences, and allows users to filter and sort papers based on download metrics and Twitter activity over a variety of categories and time periods. In this work, we hope to make it easier for readers to find relevant research on bioRxiv and to improve the visibility of preprints currently being read and discussed online

Co-depletion of NIPBL and WAPL balance cohesin activity to correct gene misexpression.

The relationship between cohesin-mediated chromatin looping and gene expression remains unclear. NIPBL and WAPL are two opposing regulators of cohesin activity; depletion of either is associated with changes in both chromatin folding and transcription across a wide range of cell types. However, a direct comparison of their individual and combined effects on gene expression in the same cell type is lacking. We find that NIPBL or WAPL depletion in human HCT116 cells each alter the expression of ~2,000 genes, with only ~30% of the genes shared between the conditions. We find that clusters of differentially expressed genes within the same topologically associated domain (TAD) show coordinated misexpression, suggesting some genomic domains are especially sensitive to both more or less cohesin. Finally, co-depletion of NIPBL and WAPL restores the majority of gene misexpression as compared to either knockdown alone. A similar set of NIPBL-sensitive genes are rescued following CTCF co-depletion. Together, this indicates that altered transcription due to reduced cohesin activity can be functionally offset by removal of either its negative regulator (WAPL) or the physical barriers (CTCF) that restrict loop-extrusion events

Pairwise correlation of PRO-seq counts.

Pairwise correlation (Spearman’s rho) of PRO-seq counts in windows ±2kb around filtered TSS annotation. N represents NIPBL knockdown, W represents WAPL knockdown, C represents CTCF knockdown, NW represents NIPBL and WAPL double knockdown, and NC represents NIPBL and CTCF double knockdown. (DOCX)</p

Additional information related to Fig 2.

(A) Top 5 GO Biological Processes scored by adjusted p-value for NIPBL DEGs and their significance. (B) Top 5 GO Biological Processes scored by adjusted p-value for WAPL DEGs and their significance. (C) Top 5 GO Biological Processes scored by adjusted p-value for RAD21 DEGs and their significance. (D) The log2(fold change) of shared DEGs across NIPBL and WAPL knockdown conditions. (E) Percentage of up, down, NIPBL, WAPL, or nonDEGs with a TSS within 5kb of a RAD21 ChIP-Seq peak co-occupied by CTCF. Fisher’s exact test, **** p (TIF)</p

Cohesin-sensitive genes are clustered and coordinated within TADs.

(A) The observed average percentage of NIPBL DEGs per TAD compared to a null distribution (expected). Permutations generated by shuffling the DEG and nonDEG designations across genes 1,000 times. Analysis limited to TADs with at least one expressed gene. Exact test, p = 0. (B) The observed average percentage of WAPL DEGs per TAD compared to a null distribution (expected). Permutations generated by shuffling the DEG and nonDEG designations across genes 1,000 times. Analysis limited to TADs with at least one expressed gene. Exact test, p = 0. (C) The average coordination of NIPBL DEGs compared to a null distribution generated by shuffling the fold change amongst the DEGs 1,000 times. Analysis limited to TADs with at least two expressed genes. Exact test, p = 0. (D) The average coordination of WAPL DEGs compared to a null distribution generated by shuffling the fold change amongst the DEGs 1,000 times. Analysis limited to TADs with at least two expressed genes. Exact test, p = 0. (E) Representative TAD with 100% DEG coordination on chr5 which contains six downregulated NIPBL DEGs. Black lines represent TADs, cyan boxes represent loops. (F) Representative TAD with 100% DEG coordination on chr17 which contains nine upregulated NIPBL DEGs. Black lines represent TADs, cyan boxes represent loops.</p

NIPBL and WAPL regulate the expression of unique and shared sets of genes.

(A) The log2(fold change) of genes after NIPBL knockdown versus their significance. DEGs are in green (999 up, 878 down) and non-significantly changed genes (nonDEGs, adjusted p-value > 0.01) are in grey. Average read density over genes from transcriptional start site (TSS) upregulated (middle) or downregulated (right) after NIPBL knockdown. (B) The log2(fold change) of genes after WAPL knockdown versus their significance. DEGs are in purple (910 up, 1022 down) and nonDEGs (adjusted p-value > 0.01) are in grey. Average read density over genes from TSS upregulated (middle) or downregulated (right) after WAPL knockdown. (C) Venn diagram of the number of NIPBL, WAPL, and shared DEGs. (D) Percentage of up, down, NIPBL, WAPL, or nonDEGs with a TSS within 5kb of a loop anchor. Fisher’s exact test compared to nonDEGs, **** p < 0.0001, *** p = 0.0002. (E) Distance from each NIPBL DEG TSS to the nearest loop anchor versus the fold change of the gene. Spearman correlation, p = 0.0018. (F) Distance from each WAPL DEG TSS to the nearest loop anchor versus the fold change of the gene. Spearman correlation, p = 0.037.</p

Oligopaint design.

Oligopaint design coordinates (hg19) and probe densities. (DOCX)</p

WAPL knockdown-associated biological processes rescued by co-depletion with NIPBL.

Top 10 GO Biological Processes for WAPL DEGs rescued in the double knockdown condition sorted by adjusted p-value. (DOCX)</p