BOL: Related items

GeNeCK (Gene Network Construction Kit) is a comprehensive online tool kit that integrate various statistical methods to construct gene networks

Rahul Nayak — Tue, 15 Jan 2019 09:39:30 -0600

GeNeCK (Gene Network Construction Kit) is a comprehensive online tool kit that integrate various statistical methods to construct gene networks based on gene expression data and optional hub gene information.

It efficiently constructs gene networks from expression data. It allows the user to use ten different network construction methods (such as partial correlation-, likelihood-, Bayesian- and mutual information-based methods) and integrates the resulting networks from multiple methods. Hub gene information, if available, can be incorporated to enhance performance.

GeNeCK is an efficient and easy-to-use web application for gene regulatory network construction. It can be accessed at http://lce.biohpc.swmed.edu/geneck

Address of the bookmark: http://lce.biohpc.swmed.edu/geneck/

MAGIC: A tool for predicting transcription factors and cofactors driving gene sets using ENCODE data

BioStar — Thu, 26 Nov 2020 11:05:04 -0600

The algorithm presented herein, Mining Algorithm for GenetIc Controllers (MAGIC), uses ENCODE ChIP-seq data to look for statistical enrichment of TFs and cofactors in gene bodies and flanking regions in gene lists without an a priori binary classification of genes as targets or non-targets. When compared to other TF mining resources, MAGIC displayed favourable performance in predicting TFs and cofactors that drive gene changes in 4 settings:

1) A cell line expressing or lacking single TF,

2) Breast tumors divided along PAM50 designations

3) Whole brain samples from WT mice or mice lacking a single TF in a particular neuronal subtype

4) Single cell RNAseq analysis of neurons divided by Immediate Early Gene expression levels.

In summary, MAGIC is a standalone application that produces meaningful predictions of TFs and cofactors in transcriptomic experiments.

More at https://uwmadison.app.box.com/s/8j90e5h2rjrsz3bacaxnq8kor2o64vyg

Address of the bookmark: https://github.com/asroopra/MAGIC

OMArk: software for proteome (protein-coding gene repertoire) quality assessment

LEGE — Wed, 21 Feb 2024 15:01:20 -0600

OMArk is a software for proteome (protein-coding gene repertoire) quality assessment. It provides measures of proteome completeness, characterizes the consistency of all protein coding genes with regard to their homologs, and identifies the presence of contamination from other species. OMArk relies on the OMA orthology database, from which it exploits orthology relationships, and on the OMAmer software for fast placement of all proteins into gene families.

Address of the bookmark: https://github.com/DessimozLab/OMArk

TwinBLAST: When Two Is Better than One

Jit — Sat, 07 Sep 2019 08:50:08 -0500

TwinBLAST is a web-based tool for viewing 2 BLAST reports simultaneouslyside-by-side. It uses ExtJS (www.sencha.com/products/extjs/) to provide 2independently scrollable panels. BioPerl (www.bioperl.org) is used to indexraw BLAST reports and Bio::Graphics is used to draw pictograms of the BLASThits.

https://github.com/IGS/twinblast

https://mra.asm.org/content/8/35/e00842-19

Address of the bookmark: https://github.com/IGS/twinblast

LoRDEC: a hybrid error correction program for long, PacBio reads

Jit — Mon, 10 Apr 2017 04:16:09 -0500

LoRDEC is a program to correct sequencing errors in long reads from 3rd generation sequencing with high error rate, and is especially intended for PacBio reads. It uses a hybrid strategy, meaning that it uses two sets of reads: the reference read set, whose error rate is assumed to be small, and the PacBio read set, which is then corrected using the reference set. Typically, the reference set contains Illumina reads.

Usually, errors in PacBio reads include many insertions and deletions, and comparatively less substitutions. LoRDEC can correct errors of all these types.
After correction, a larger portion of the sequence of PacBio reads is usable for detection of region of similarity with other sequences, for aligning them to the contigs of an assembly, etc.

Why is LoRDEC different?

It is efficient and can process large read data sets, included from eukaryotic or vertebrate species, on a usual computing server, and even works on desktop/laptop computers.
It adopts a novel graph based approach: it builds a succinct De Bruijn Graph (DBG) representing the short reads, and seeks a corrective sequence for each erroneous region of a long read by traversing chosen paths in the graph.

Address of the bookmark: http://www.atgc-montpellier.fr/lordec/

HECIL: A Hybrid Error Correction Algorithm for Long Reads with Iterative Learning

Abhimanyu Singh — Tue, 01 Jan 2019 12:01:00 -0600

HECIL—Hybrid Error Correction with Iterative Learning—a hybrid error correction framework that determines a correction policy for erroneous long reads, based on optimal combinations of decision weights obtained from short read alignments.

HECIL’s core algorithm by introducing an iterative learning paradigm that enhances the correction policy at each iteration by incorporating knowledge gathered from previous iterations via data-driven confidence metrics assigned to prior corrections.

Address of the bookmark: https://github.com/NDBL/HECIL

Hercules: a profile HMM-based hybrid error correction algorithm for long reads

Jit — Mon, 20 Aug 2018 14:14:11 -0500

Choosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several studies require long and accurate reads including de novo assembly, fusion and structural variation detection. In such cases researchers often combine both technologies and the more erroneous long reads are corrected using the short reads. Current approaches rely on various graph based alignment techniques and do not take the error profile of the underlying technology into account. Memory- and time- efficient machine learning algorithms that address these shortcomings have the potential to achieve better and more accurate integration of these two technologies. Results: We designed and developed Hercules, the first machine learning-based long read error correction algorithm. The algorithm models every long read as a profile Hidden Markov Model with respect to the underlying platformtextquoterights error profile. The algorithm learns a posterior transition/emission probability distribution for each long read and uses this to correct errors in these reads. Using datasets from two DNA-seq BAC clones (CH17-157L1 and CH17-227A2), and human brain cerebellum polyA RNA-seq, we show that Hercules-corrected reads have the highest mapping rate among all competing algorithms and highest accuracy when most of the basepairs of a long read are covered with short reads. Availability:

Hercules source code is available at https://github.com/BilkentCompGen/Hercules

Address of the bookmark: https://github.com/BilkentCompGen/Hercules

Prokka: tool for the rapid annotation of prokaryotic genomes

Jit — Mon, 06 Mar 2017 03:49:57 -0600

Prokka is a software tool for the rapid annotation of prokaryotic genomes. A typical 4 Mbp genome can be fully annotated in less than 10 minutes on a quad-core computer, and scales well to 32 core SMP systems. It produces GFF3, GBK and SQN files that are ready for editing in Sequin and ultimately submitted to Genbank/DDJB/ENA.

Address of the bookmark: http://www.vicbioinformatics.com/software.prokka.shtml

DEG 5.0: a database of essential genes in both prokaryotes and eukaryotes

Rahul Nayak — Tue, 30 Mar 2021 11:47:28 -0500

Essential genes are those indispensable for the survival of an organism, and their functions are therefore considered a foundation of life. Determination of a minimal gene set needed to sustain a life form, a fundamental question in biology, plays a key role in the emerging field, synthetic biology.

DEG is freely available at the website http://tubic.tju.edu.cn/deg or http://www.essentialgene.org.

Address of the bookmark: http://www.essentialgene.org/