GoPeaks: Histone Modification Peak Calling for CUT&Tag

Genome-wide mapping of the histone modification landscape is critical to understanding tran-scriptional regulation. Cleavage Under Targets and Tagmentation (CUT&Tag) is a new method for profiling the localization of covalent histone modifications, offering improved sensitivity and decreased cost compared with Chromatin Immunoprecipitation Sequencing (ChIP-seq). Here, we present GoPeaks, a peak calling method specifically designed for histone modification CUT&Tag data. GoPeaks implements a Binomial distribution and stringent read count cut-off to nominate candidate genomic regions. We compared the performance of GoPeaks against com-monly used peak calling algorithms to detect H3K4me3, H3K4me1, and H3K27Ac peaks from CUT&Tag data. These histone modifications display a range of peak profiles and are frequently used in epigenetic studies. We found GoPeaks robustly detects genome-wide histone modifica-tions and, notably, identifies H3K27Ac with improved sensitivity compared to other standard peak calling algorithms.

AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data

Detecting multiplets in single nucleus (sn)ATAC-seq data is challenging due to data sparsity and limited dynamic range. AMULET (ATAC-seq MULtiplet Estimation Tool) enumerates regions with greater than two uniquely aligned reads across the genome to effectively detect multiplets. We evaluate the method by generating snATAC-seq data in the human blood and pancreatic islet samples. AMULET has high precision, estimated via donor-based multiplexing, and high recall, estimated via simulated multiplets, compared to alternatives and identifies multiplets most effectively when a certain read depth of 25K median valid reads per nucleus is achieved.

VISPR-online: a web-based interactive tool to visualize CRISPR screening experiments

Background VISPR is an interactive visualization and analysis framework for CRISPR screening experiments. However, it only supports the output of MAGeCK, and requires installation and manual configuration. Furthermore, VISPR is designed to run on a single computer, and data sharing between collaborators is challenging. Results To make the tool easily accessible to the community, we present VISPR-online, a web-based general application allowing users to visualize, explore, and share CRISPR screening data online with a few simple steps. VISPR-online provides an exploration of screening results and visualization of read count changes. Apart from MAGeCK, VISPR-online supports two more popular CRISPR screening analysis tools: BAGEL and JACKS. It provides an interactive environment for exploring gene essentiality, viewing guide RNA (gRNA) locations, and allowing users to resume and share screening results. Conclusions VISPR-online allows users to visualize, explore and share CRISPR screening data online. It is freely available at http://vispr-online.weililab.org, while the source code is available at https://github.com/lemoncyb/VISPR-online.

RNA-Ribo Explorer: interactive mining and visualisation of Ribosome profiling data

RNA translation has long been thought as a stable and uniform process by which a ribosome produces a protein encoded by the main Open Reading Frame (ORF) of an mRNA. Recently, growing evidence support incomplete correlation between RNA and protein abundance levels, the existence of alternative ORFs in numerous mammalian RNAs, and the involvement of ribosomes in gene expression regulation, thereby challenging previous views of translation. Ribosome profiling (aka Ribo-seq) has renewed the study of translation by enabling the mapping of translating ribosomes on the whole transcriptome using deep sequencing. Despite increasing use of Ribo-seq, recent review articles conclude that flexible, interactive tools for mining such data are missing. As Ribo-seq protocols still evolve, flexibility is highly desirable for the end-user. Here we describe RNA-Ribo-Explorer (RRE) a stand-alone tool that fills this gap. With RRE, one can explore read-count profiles of RNAs obtained after mapping, compare them between conditions, and visualize the profiles of individual RNAs. Importantly, the user can mine the data by defining queries that combine several criteria to detect interesting subsets of RNAs. For instance, one can ask RRE to find all RNAs whose translation of UTR region compared to that of the main ORF has changed between two conditions. This feature seems useful for finding candidate RNAs whose translation status or processing has changed across conditions. RRE is a platform independent software and is freely available at https://gite.lirmm.fr/rivals/RRE/-/releases.

A read count-based method to detect multiplets and their cellular origins from snATAC-seq data

ABSTRACTSimilar to other droplet-based single cell assays, single nucleus ATAC-seq (snATAC-seq) data harbor multiplets that confound downstream analyses. Detecting multiplets in snATAC-seq data is particularly challenging due to its sparsity and trinary nature (0 reads: closed chromatin, 1: open in one allele, 2: open in both alleles), yet offers a unique opportunity to infer multiplets when >2 uniquely aligned reads are observed at multiple loci. Here, we implemented the first read count-based multiplet detection method, ATAC-DoubletDetector, that detects multiplets independently of cell-type. Using PBMC and pancreatic islet datasets, ATAC-DoubletDetector captured simulated heterotypic multiplets (different cell-types) with ∼0.60 recall, showing ∼24% improvement over state of the art. ATAC-DoubletDetector detected homotypic multiplets with ∼0.61 recall, representing the first method to detect multiplets originating from the same cell type. Using our novel clustering-based algorithm, multiplets were annotated to their cellular origins with ∼85% accuracy. Application of ATAC-DoubletDetector will improve downstream analysis of snATAC-seq.

Whole Genome Sequencing of Sunflower Root-Associated Bacillus cereus

In recent times, diverse agriculturally important endophytic bacteria colonizing plant endosphere have been identified. Harnessing the potential of Bacillus species from sunflower could reveal their biotechnological and agricultural importance. Here, we present genomic insights into B. cereus T4S isolated from sunflower sourced from Lichtenburg, South Africa. Genome analysis revealed a sequence read count of 7 255 762, a genome size of 5 945 881 bp, and G + C content of 34.8%. The genome contains various protein-coding genes involved in various metabolic pathways. The detection of genes involved in the metabolism of organic substrates and chemotaxis could enhance plant-microbe interactions in the synthesis of biological products with biotechnological and agricultural importance.

The plot thickens: haploid and triploid-like thalli, hybridization, and biased mating type ratios in Letharia

The study of the reproductive biology of lichen fungal symbionts has been traditionally challenging due to their complex and symbiotic lifestyles. Against the common belief of haploidy, a recent genomic study found a triploid-like signal in Letharia. Here, we used genomic data from a pure culture and from thalli, together with a PCR survey of the MAT locus, to infer the genome organization and reproduction in Letharia. We found that the read count variation in the four Letharia specimens, including the pure culture derived from a single sexual spore of L. lupina, is consistent with haploidy. By contrast, the L. lupina read counts from a thallus’ metagenome are triploid-like. Characterization of the mating-type locus revealed a conserved heterothallic configuration across the genus, along with auxiliary genes that we identified. We found that the mating-type distributions are balanced in North America for L. vulpina and L. lupina, suggesting widespread sexual reproduction, but highly skewed in Europe for L. vulpina, consistent with predominant asexuality. Taken together, we propose that Letharia fungi are heterothallic and typically haploid, and provide evidence that triploid-like individuals are rare hybrids between L. lupina and an unknown Letharia lineage, reconciling classic systematic and genetic studies with recent genomic observations.

FITs: forest of imputation trees for recovering true signals in single-cell open chromatin profiles

The advent of single-cell open-chromatin profiling technology has facilitated the analysis of heterogeneity of activity of regulatory regions at single-cell resolution. However, stochasticity and availability of low amount of relevant DNA, cause high drop-out rate and noise in single-cell open-chromatin profiles. We introduce here a robust method called as forest of imputation trees (FITs) to recover original signals from highly sparse and noisy single-cell open-chromatin profiles. FITs makes multiple imputation trees to avoid bias during the restoration of read-count matrices. It resolves the challenging issue of recovering open chromatin signals without blurring out information at genomic sites with cell-type-specific activity. Besides visualization and classification, FITs-based imputation also improved accuracy in the detection of enhancers, calculating pathway enrichment score and prediction of chromatin-interactions. FITs is generalized for wider applicability, especially for highly sparse read-count matrices. The superiority of FITs in recovering signals of minority cells also makes it highly useful for single-cell open-chromatin profile from in vivo samples. The software is freely available at https://reggenlab.github.io/FITs/.

FITs: Forest of imputation trees for recovering true signals in single-cell open chromatin profiles

The advent of single-cell open-chromatin profiling technology has facilitated the analysis of heterogeneity of activity of regulatory regions at single-cell resolution. However, stochasticity and availability of low amount of relevant DNA cause high drop-out rate and noise in single-cell open-chromatin profiles. We introduce here a robust method called as Forest of Imputation Trees (FITs) to recover original signals from highly sparse and noisy single-cell open-chromatin profiles. FITs makes a forest of imputation trees to avoid bias during the restoration of read-count matrices. It resolves the challenging issue of recovering open chromatin signals without blurring out information at genomic sites with cell-type-specific activity. FITs is generalized for wider applicability, especially for highly sparse read-count matrices. The superiority of FITs in recovering signals of minority cells also makes it highly useful for single-cell open-chromatin profile from in vivo samples.First made online as thesis work at https://repository.iiitd.edu.in/xmlui/handle/123456789/807