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MITObim - mitochondrial baiting and iterative mapping

cn.mops (Copy Number estimation by a Mixture Of PoissonS) is a data processing pipeline for copy number variations and aberrations (CNVs and CNAs) from next generation sequencing (NGS) data. The package supplies functions to convert BAM files into read count matrices or genomic ranges objects, which are the input objects for cn.mops. cn.mops models the depths of coverage across samples at each genomic position. Therefore, it does not suffer from read count biases along chromosomes. Using a Bayesian approach, cn.mops decomposes read variations across samples into integer copy numbers and noise by its mixture components and Poisson distributions, respectively. cn.mops guarantees a low FDR because wrong detections are indicated by high noise and filtered out. cn.mops is very fast and written in C++.

This package provides an R wrapper of the popular bowtie2 sequencing reads aligner and AdapterRemoval, a convenient tool for rapid adapter trimming, identification, and read merging. The package contains wrapper functions that allow for genome indexing and alignment to those indexes. The package also allows for the creation of .bam files via Rsamtools.

The package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene's differential expression and splicing, respectively.

The soGGi package provides a toolset to create genomic interval aggregate/summary plots of signal or motif occurence from BAM and bigWig files as well as PWM, rlelist, GRanges and GAlignments Bioconductor objects. soGGi allows for normalisation, transformation and arithmetic operation on and between summary plot objects as well as grouping and subsetting of plots by GRanges objects and user supplied metadata. Plots are created using the GGplot2 libary to allow user defined manipulation of the returned plot object. Coupled together, soGGi features a broad set of methods to visualise genomics data in the context of groups of genomic intervals such as genes, superenhancers and transcription factor binding events.

Browser-based viewer for Sanger sequencing chromatograms in AB1/ABIF and SCF format, and for the .srd raw files of the Nanofor-05 capillary sequencer, converted to ABIF as they open. Shows raw and analysed traces, base calls (called or edited), per-base quality and the full ABIF directory, and puts two reads side by side with their tag tables aligned. A read aligns against a pasted or loaded reference, which reports identity, mismatches, indels and the ends that did not align, and flips the strand when the read is on the other one. End trimming (modified Mott or sliding window) has draggable handles, motif search is IUPAC-aware and finds primers, and QC metrics copy out as CSV. Drag on a trace to select a base range, then copy it as FASTA, zoom to it or export just that region. Exports the read as FASTA, FASTQ, .qual or ABIF and the chromatogram as a high-resolution PNG. Open files are kept as local sessions. Runs entirely in the browser — files are never uploaded.

Supernova is a software package for de novo assembly from Chromium Linked-Reads that are made from a single whole-genome library from an individual DNA source. A key feature of Supernova is that it creates diploid assemblies, thus separately representing maternal and paternal chromosomes over very long distances. Almost all other methods instead merge homologous chromosomes into single incorrect 'consensus' sequences. Supernova is the only practical method for creating diploid assemblies of large genomes.

Browser-based viewer that maps sequencing reads onto one short reference — an amplicon, gene or plasmid. Reads open as Sanger AB1/ABIF, SCF, FASTA, FASTQ or a SAM somebody else already mapped (gzipped files are unpacked in place); the reference as FASTA, GenBank or a read. Both read orientations are tried automatically. The pileup reports per-position depth, where reads disagree with the target, and the consensus — phred-weighted for capillary reads, which keep their chromatogram under the letters. An optional protein lane translates target and consensus side by side. Reads are placed by minimap2 compiled to WebAssembly, or by the built-in aligner. One read or the whole alignment saves as FASTA. Runs entirely in the browser — files are never uploaded.