BOL: Related items

3d-dna: 3D de novo assembly (3D DNA) pipeline

Jit — Thu, 28 Dec 2017 10:09:37 -0600

This code is designed to enable anyone to reproduce the Hs2-HiC and the AaegL4 genomes reported in: Dudchenko et al., De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science, 2017.

Unless otherwise noted, all terminology below is consistent with this paper, and all references to figures and tables in this readme refer to this paper. Specifically, some of the terminology used below is outlined in Figure S2. The assembly procedure is described in detail in the Supporting Online Materials, specifically in the section labelled “Pipeline description”.

In addition, the pipeline uses tools and methods from Juicer (Durand & Shamim et al., Cell Systems, 2016) and Juicebox (Durand & Robinson et al., Cell Systems, 2016), as well as additional dependencies noted below.

Feel free to post your questions and comments at: http://www.aidenlab.org/forum.html

http://aidenlab.org/documentation.html

Address of the bookmark: https://github.com/theaidenlab/3d-dna

MCAT: Motif Combining and Association Tool

Neel — Sun, 13 Jan 2019 06:27:28 -0600

This is a pipeline for finding motifs in fasta files.
It can be run from the command line as follows:

usage: orange_pipeline_refine.py [-h] [-w W] [--nmotifs NMOTIFS] [--iter ITER] [-c C]
[-s S] [-d] [-ff] [-v V]
positive_seq negative_seq

positional arguments:
positive_seq the fasta file for the positive sequences
negative_seq the fasta file for the negative sequences

Address of the bookmark: https://github.com/yanshen43/MCAT

dnaPipeTE: a pipeline designed to find, annotate and quantify Transposable Elements

Jit — Mon, 12 Aug 2019 21:56:08 -0500

dnaPipeTE (for de-novo assembly & annotation Pipeline for Transposable Elements), is a pipeline designed to find, annotate and quantify Transposable Elements in small samples of NGS datasets. It is very useful to quantify the proportion of TEs in newly sequenced genomes since it does not require genome assembly and works on small datasets (< 1X).

https://github.com/clemgoub/dnaPipeTE/wiki/dnaPipeTE-WIKI-home

Address of the bookmark: https://github.com/clemgoub/dnaPipeTE

DeepVariant : an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

Jit — Sat, 25 Jan 2020 13:28:09 -0600

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant relies on Nucleus, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the TensorFlow machine learning framework.

https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html

https://www.biorxiv.org/content/10.1101/092890v6

Address of the bookmark: https://github.com/google/deepvariant

sunbeam: A robust, extensible metagenomics pipeline

Jit — Thu, 18 Jun 2020 06:58:52 -0500

Sunbeam is a pipeline written in snakemake that simplifies and automates many of the steps in metagenomic sequencing analysis. It uses conda to manage dependencies, so it doesn't have pre-existing dependencies or admin privileges, and can be deployed on most Linux workstations and clusters. To read more, check out our paper in Microbiome.

https://sunbeam.readthedocs.io/en/latest/

Address of the bookmark: https://github.com/sunbeam-labs/sunbeam

Liftoff: An accurate GFF3/GTF lift over pipeline

Neel — Sun, 20 Dec 2020 01:36:37 -0600

Liftoff is a tool that accurately maps annotations in GFF or GTF between assemblies of the same, or closely-related species. Unlike current coordinate lift-over tools which require a pre-generated “chain” file as input, Liftoff is a standalone tool that takes two genome assemblies and a reference annotation as input and outputs an annotation of the target genome.

Address of the bookmark: https://github.com/agshumate/Liftoff

JUDI: Just Do It

Jit — Mon, 06 Sep 2021 02:44:35 -0500

judi comes from the idea of bringing the power and efficiency of doit to execute any kind of task under many combinations of parameter settings.

https://github.com/ncbi/JUDI

Address of the bookmark: https://github.com/ncbi/JUDI

PhyloHerb: A high‐throughput phylogenomic pipeline for processing genome skimming data

Abhi — Wed, 06 Sep 2023 00:14:28 -0500

Phylogenomic Analysis Pipeline for Herbarium Specimens

What is PhyloHerb: PhyloHerb is a wrapper program to process genome skimming data collected from plant materials. The outcomes include the plastid genome (plastome) assemblies, mitochondrial genome assemblies, nuclear ribosomal DNAs (NTS+ETS+18S+ITS1+5.8S+ITS2+28S), alignments of gene and intergenic regions, and a species tree. It is designed to be a high throughput program dealing with lower quality data. Examples include low-coverage (5x cpDNA) plastome phylogeny, recycling plastid genes from target enrichment data, retrieving low-copy nuclear genes from medium coverage (5x nucDNA) genome skimming.

License: GNU General Public License

Citation:

Cai, Liming, Hongrui Zhang, and Charles C. Davis. 2022. PhyloHerb: A high‐throughput phylogenomic pipeline for processing genome‐skimming data. Applications in Plant Sciences 10(3): 1–9. https://doi.org/10.1002/aps3.11475

Address of the bookmark: https://github.com/lmcai/PhyloHerb/

iMAGine - in silico MetAGenomics pipeline

BioStar — Sat, 06 Jul 2024 04:32:18 -0500

iMAGine is a metagenomic workflow which includes filtering, assembling, and binning.

This workflow includes the following tools which are needed to be installed in the system.

Address of the bookmark: https://github.com/avishekdutta14/iMAGine

Kaiju

Jit — Mon, 27 Jun 2016 11:23:04 -0500

Kaiju is a program for the taxonomic classification of metagenomic high-throughput sequencing reads. Each read is directly assigned to a taxon within the NCBI taxonomy by comparing it to a reference database containing microbial and viral protein sequences.

By default, Kaiju uses either the available complete genomes from NCBI RefSeq or the microbial subset of the non-redundant protein database nr used by NCBI BLAST, optionally also including fungi and microbial eukaryotes.

Kaiju translates reads into amino acid sequences, which are then searched in the database using a modified backward search on a memory-efficient implementation of the Burrows-Wheeler transform, which finds maximum exact matches (MEMs), optionally allowing mismatches in the protein alignment. The search can process up to millions of reads per minute using, for example, only 10 GB RAM with a protein database comprising 4821 microbial genomes. Kaiju can also be used for querying any other protein database without taxonomic classification, using either protein or nucleotide queries.

Kaiju is described in Menzel, P. et al. (2016) Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7:11257 (open access).

Address of the bookmark: http://kaiju.binf.ku.dk/