MOTIVATION: The role of repetitive DNA in the 3D organization of the interphase nucleus is a subject of intensive study. In studies of 3D nucleus organization, mutual contacts of various loci can be identified by Hi-C sequencing. Typical analyses use binning of read pairs by location to reduce noise. We use binning by repeat families instead to make similar conclusions about repeat regions. RESULTS: To achieve this, we combined Hi-C data, reference genome data and tools for repeat analysis into a Nextflow pipeline identifying and quantifying the contacts of specific repeat families. As an output, our pipeline produces heatmaps showing contact frequency and circular diagrams visualizing repeat contact localization. Using our pipeline with tomato data, we revealed the preferential homotypic interactions of ribosomal DNA, centromeric satellites and some LTR retrotransposon families and, as expected, little contact between organellar and nuclear DNA elements. While the pipeline can be applied to any eukaryotic genome, results in plants provide better coverage, since the built-in TE-greedy-nester software only detects tandems and LTR retrotransposons. Other repeats can be fed via GFF3 files. This pipeline represents a novel and reproducible way to analyze the role of repetitive elements in the 3D organization of genomes. AVAILABILITY AND IMPLEMENTATION: https://gitlab.fi.muni.cz/lexa/hic-te/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Biology is increasingly digital, and scientists are generating huge amounts of data daily, turning molecules into sequences and text files.As a biologist, you might need help analyzing all these data and have considered collaborating with a computer scientist for the first, second, or third time.This person might have some training in computational biology, but their main focus has always been computer science (CS), and here is the challenge: how do you talk to them?They might be able to write efficient code, but they often do not know some of the basics of biology.When they look at your molecules, some of them might see text files before biology.Also, if explaining things takes so much time, is it worth it?Should you be analyzing your own data instead?Or perhaps you have noticed that all those big, shiny papers of today represent a smart blend of biology and CS.You have found a collaborator and want to learn how to engage them.These 10 simple rules aim to help. Rule 2: Do not judge knowledge gapsEven the lack of most basic knowledge is okay.People can live happy lives without ever knowing what a centromere is or understanding the difference between meiosis and mitosis.If you

SUMMARY: Tandem DNA repeats can be sequenced with long-read technologies, but cannot be accurately deciphered due to the lack of computational tools taking high error rates of these technologies into account. Here we introduce Noise-Cancelling Repeat Finder (NCRF) to uncover putative tandem repeats of specified motifs in noisy long reads produced by Pacific Biosciences and Oxford Nanopore sequencers. Using simulations, we validated the use of NCRF to locate tandem repeats with motifs of various lengths and demonstrated its superior performance as compared to two alternative tools. Using real human whole-genome sequencing data, NCRF identified long arrays of the (AATGG)n repeat involved in heat shock stress response. AVAILABILITY AND IMPLEMENTATION: NCRF is implemented in C, supported by several python scripts, and is available in bioconda and at https://github.com/makovalab-psu/NoiseCancellingRepeatFinder. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: The haploid mammalian Y chromosome is usually under-represented in genome assemblies due to high repeat content and low depth due to its haploid nature. One strategy to ameliorate the low coverage of Y sequences is to experimentally enrich Y-specific material before assembly. As the enrichment process is imperfect, algorithms are needed to identify putative Y-specific reads prior to downstream assembly. A strategy that uses k-mer abundances to identify such reads was used to assemble the gorilla Y. However, the strategy required the manual setting of key parameters, a time-consuming process leading to sub-optimal assemblies. Results: We develop a method, RecoverY, that selects Y-specific reads by automatically choosing the abundance level at which a k-mer is deemed to originate from the Y. This algorithm uses prior knowledge about the Y chromosome of a related species or known Y transcript sequences. We evaluate RecoverY on both simulated and real data, for human and gorilla, and investigate its robustness to important parameters. We show that RecoverY leads to a vastly superior assembly compared to alternate strategies of filtering the reads or contigs. Compared to the preliminary strategy used by Tomaszkiewicz et al., we achieve a 33% improvement in assembly size and a 20% improvement in the NG50, demonstrating the power of automatic parameter selection. Availability and implementation: Our tool RecoverY is freely available at https://github.com/makovalab-psu/RecoverY. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Sex chromosomes present a genomic region which to some extent, differs between the genders of a single species. Reliable high-throughput methods for detection of sex chromosomes specific markers are needed, especially in species where genome information is limited. Next generation sequencing (NGS) opens the door for identification of unique sequences or searching for nucleotide polymorphisms between datasets. A combination of classical genetic segregation analysis along with RNA-Seq data can present an ideal tool to map and identify sex chromosome-specific expressed markers. To address this challenge, we established genetic cross of dioecious plant Rumex acetosa and generated RNA-Seq data from both parental generation and male and female offspring. RESULTS: We present a pipeline for detection of sex linked genes based on nucleotide polymorphism analysis. In our approach, tracking of nucleotide polymorphisms is carried out using a cross of preferably distant populations. For this reason, only 4 datasets are needed - reads from high-throughput sequencing platforms for parent generation (mother and father) and F1 generation (male and female progeny). Our pipeline uses custom scripts together with external assembly, mapping and variant calling software. Given the resource-intensive nature of the computation, servers with high capacity are a requirement. Therefore, in order to keep this pipeline easily accessible and reproducible, we implemented it in Galaxy - an open, web-based platform for data-intensive biomedical research. Our tools are present in the Galaxy Tool Shed, from which they can be installed to any local Galaxy instance. As an output of the pipeline, user gets a FASTA file with candidate transcriptionally active sex-linked genes, sorted by their relevance. At the same time, a BAM file with identified genes and alignment of reads is also provided. Thus, polymorphisms following segregation pattern can be easily visualized, which significantly enhances primer design and subsequent steps of wet-lab verification. CONCLUSIONS: Our pipeline presents a simple and freely accessible software tool for identification of sex chromosome linked genes in species without an existing reference genome. Based on combination of genetic crosses and RNA-Seq data, we have designed a high-throughput, cost-effective approach for a broad community of scientists focused on sex chromosome structure and evolution.