Joint changes in RNA, RNA polymerase II, and promoter activity through the cell cycle identify non-coding RNAs involved in proliferation.

Proper regulation of the cell cycle is necessary for normal growth and development of all organisms. Conversely, altered cell cycle regulation often underlies proliferative diseases such as cancer. Long non-coding RNAs (lncRNAs) are recognized as important regulators of gene expression and are often found dysregulated in diseases, including cancers.

The causal effects of serum lipids and apolipoproteins on kidney function: multivariable and bidirectional Mendelian-randomization analyses.

Background: The causal nature of the observed associations between serum lipids and apolipoproteins and kidney function are unclear.

Methods: Using two-sample and multivariable Mendelian randomization (MR), we examined the causal effects of serum lipids and apolipoproteins on kidney function, indicated by the glomerular-filtration rate estimated using creatinine (eGFRcrea) or cystatin C (eGFRcys) and the urinary albumin-to-creatinine ratio (UACR). We obtained lipid- and apolipoprotein-associated genetic variants from the Global Lipids Genetics Consortium (n = 331 368) and UK Biobank (n = 441 016), respectively, and kidney-function markers from the Trøndelag Health Study (HUNT; n = 69 736) and UK Biobank (n = 464 207).

Methplotlib: analysis of modified nucleotides from nanopore sequencing.

Summary: Modified nucleotides play a crucial role in gene expression regulation. Here, we describe methplotlib, a tool developed for the visualization of modified nucleotides detected from Oxford Nanopore Technologies sequencing platforms, together with additional scripts for statistical analysis of allele-specific modification within-subjects and differential modification frequency across subjects.

Availability And Implementation: The methplotlib command-line tool is written in Python3, is compatible with Linux, Mac OS and the MS Windows 10 Subsystem for Linux and released under the MIT license.

PyRanges: efficient comparison of genomic intervals in Python.

Summary: Complex genomic analyses often use sequences of simple set operations like intersection, overlap and nearest on genomic intervals. These operations, coupled with some custom programming, allow a wide range of analyses to be performed. To this end, we have written PyRanges, a data structure for representing and manipulating genomic intervals and their associated data in Python.

epic2 efficiently finds diffuse domains in ChIP-seq data.

Summary: Data from chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-seq) generally contain either narrow peaks or broad and diffusely enriched domains. The SICER ChIP-seq caller has proven adept at finding diffuse domains in ChIP-seq data, but it is slow, requires much memory, needs manual installation steps and is hard to use. epic2 is a complete rewrite of SICER that is focused on speed, low memory overhead and ease-of-use.