Tool Library list page for genomics topic with tag 'Genomics'



10 tools

BEDTools is an extensive suite of utilities for comparing genomic features in BED format.

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A tool that finds regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance.

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Bowtie is an ultrafast, memory-efficient short read aligner.

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Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (e.g. mammalian) genomes. Bowtie 2 indexes the genome with an FM Index to keep its memory footprint small: for the human genome, its memory footpri

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Find and remove adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads. Cleaning your data in this way is often required: Reads from small-RNA sequencing contain the 3’ sequencing adapter because the read is longer than the molecule that is sequenced. Amplicon reads start with a primer sequence. Poly-A tails are useful for pulling o

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User-friendly tools for the normalization and visualization of deep-sequencing data.

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User-friendly tools for the normalization and visualization of deep-sequencing data.

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Tool for predicting effects of variants for any genome in Ensembl or with genome annotation (via GFF). This includes vertebrates and also plants, fungi, protists, metazoa and bacteria. There is a web and a REST API version but the most powerful is the Perl script version. See McLaren et al., 2016, Genome Biology

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Bayesian genetic variant detector designed to find small polymorphisms, specifically SNPs, indels, multi-nucleotide polymorphisms, and complex events (composite insertion and substitution events) smaller than the length of a short-read sequencing alignment.

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This package is aimed at predicting and number of distinct reads and how many will be expected from additional sequencing using an initial sequencing experiment. The estimates can then be used to examine the utility of further sequencing, optimize the sequencing depth, or to screen multiple libraries to avoid low complexity samples.

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