BOL: Related items

CLA: Contig-Layout-Authenticator

Jit — Fri, 05 May 2017 05:58:36 -0500

To improve upon the shortcomings associated with the construction of draft genomes with Illumina paired-end sequencing, we developed Contig-Layout-Authenticator (CLA). The CLA pipeline can scaffold reference-sorted contigs based on paired reads, resulting in better assembled genomes. Moreover, CLA also hints at probable misassemblies and contaminations, for the users to cross-check before constructing the consensus draft. The CLA pipeline was designed and trained extensively on various bacterial genome datasets for the ordering and scaffolding of large repetitive contigs. The tool has been validated and compared favorably with other widely-used scaffolding and ordering tools using both simulated and real sequence datasets. CLA is a user friendly tool that requires a single command line input to generate ordered scaffolds.

Script https://sourceforge.net/projects/c-l-authenticator/files/

Address of the bookmark: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0155459

Synteny Portal: a web-based application portal for synteny block analysis

Jit — Wed, 24 May 2017 10:39:23 -0500

Synteny Portal, a versatile web-based application portal for constructing, visualizing and browsing synteny blocks. With Synteny Portal, users can easily (i) construct synteny blocks among multiple species by using prebuilt alignments in the UCSC genome browser database, (ii) visualize and download syntenic relationships as high-quality images, (iii) browse synteny blocks with genetic information and (iv) download the details of synteny blocks to be used as input for downstream synteny-based analyses, all in an intuitive and easy-to-use web-based interface. We believe that Synteny Portal will serve as a highly valuable tool that will enable biologists to easily perform comparative genomics studies by compensating limitations of existing tools. Synteny Portal is freely available at http://bioinfo.konkuk.ac.kr/synteny_portal.

http://bioinfo.konkuk.ac.kr/synteny_portal/

Address of the bookmark: http://bioinfo.konkuk.ac.kr/synteny_portal/

NAViGaTOR: Network Analysis, Visualization and Graphing Toronto

Rahul Nayak — Tue, 03 Jul 2018 05:05:55 -0500

NAViGaTOR – Network Analysis, Visualization, & Graphing TORonto is a software system for scaleable visualizing and analyzing networks. The current version, NAViGaTOR 3, increases modularity, improves scaleability, extends input/output options, brings new network views and analysis algorithms. http://142.150.188.236/navigatorwp/

Address of the bookmark: http://142.150.188.236/navigatorwp/

RAVEN: a software suite for Matlab that allows for semi-automated reconstruction of genome-scale models

Jit — Wed, 24 Oct 2018 22:38:05 -0500

The RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox 2 is a software suite for Matlab that allows for semi-automated reconstruction of genome-scale models (GEMs). It makes use of published models and/or KEGG, MetaCyc databases, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology.

Address of the bookmark: https://github.com/SysBioChalmers/RAVEN

ShinyGO v0.61: Gene Ontology Enrichment Analysis + more

Jit — Wed, 03 Jun 2020 08:00:30 -0500

2/3/2020: Now published by Bioinformatics.

11/3/2019: V 0.61, Improve graphical visualization (thanks to reviewers). Interactive networks and much more.

5/20/2019: V.0.60, Annotation database updated to Ensembl 96. New bacterial and fungal genomes based on STRING-db! Just paste your gene list to get enriched GO terms and othe pathways for over 315 plant and animal species, based on annotation from Ensembl (Release 96), Ensembl plants (R. 43) and Ensembl Metazoa (R. 43). An additional 2031 genomes (including bacteria and fungi) are annotated based on STRING-db (v.10). In addition, it also produces KEGG pathway diagrams with your genes highlighted, hierarchical clustering trees and networks summarizing overlapping terms/pathways, protein-protein interaction networks, gene characterristics plots, and enriched promoter motifs.

Address of the bookmark: http://bioinformatics.sdstate.edu/go/

Understanding GO analysis

Neel — Wed, 08 Feb 2023 04:22:01 -0600

The confusion about gene ontology and gene ontology analysis can start right from the term itself. There are actually two different entities that are commonly referred to as gene ontology or “GO”:

the ontology itself, which is a set of terms with their precise definitions and defined relationships between them, and
the associations between gene products and GO terms, which are used to capture the existing knowledge about what each gene is known to do.

But the term gene ontology, or GO, is commonly used to refer to both, which is sometimes a source of potential confusion. In order to avoid this, here we will use the term “GO ontology” to describe the set of terms and their hierarchical structure and “GO annotations” to describe the set of associations between genes and GO terms.

There are 3 types of terms, or domains if you wish, in the gene ontology:

Biological Processes (BP)
Molecular Functions (MF)
Cellular Components (CC)

Address of the bookmark: https://advaitabio.com/faq-items/understanding-gene-ontology/

DIY Transcriptomics

Abhi — Wed, 31 Jul 2024 01:19:26 -0500

A semester-long course covering best practices for the analysis of high-throughput sequencing data from gene expression (RNA-seq) studies, with a primary focus on empowering students to be independent in the use of lightweight and open-source software using the R programming language and the Bioconductor suite of packages. This course follows a hybrid format in which online lectures are paired with in-person labs where students participate in hands-on, live coding exercises using real ‘omic datasets. The course is focused on datasets and topics central to infectious disease research, immunology, and One-Health, but the concepts and approaches covered are applicable to any genomic study.

https://diytranscriptomics.com

Address of the bookmark: https://diytranscriptomics.com

PeGAS: A Comprehensive Bioinformatic Solution for Pathogenic Bacterial Genomic Analysis

LEGE — Mon, 01 Sep 2025 01:18:10 -0500

This is PeGAS, a powerful bioinformatic tool designed for the seamless quality control, assembly, and annotation of Illumina paired-end reads specific to pathogenic bacteria. This tool integrates state-of-the-art open-source software to provide a streamlined and efficient workflow, ensuring accurate insights into the genomic makeup of pathogenic microbial strains.

Address of the bookmark: https://github.com/liviurotiul/PeGAS

Single Cell RNAseq data analysis tutorial !!

Robert M Willioms — Mon, 27 Nov 2017 16:24:29 -0600

A major breakthrough (replaced microarrays) in the late 00’s and has been widely used since
Measures the average expression level for each gene across a large population of input cells
Useful for comparative transcriptomics, e.g. samples of the same tissue from different species
Useful for quantifying expression signatures from ensembles, e.g. in disease studies
Insufficient for studying heterogeneous systems, e.g. early development studies, complex tissues (brain)
Does not provide insights into the stochastic nature of gene expression

Following are the useful links:

Single Cell RNAseq data analysis Tutorial

A step-by-step workflow for low-level analysis of single-cell RNA-seq data

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

SCell: single-cell RNA-seq analysis software

https://github.com/diazlab/SCell

Beta-Poisson model for single-cell RNA-seq data analyses

https://github.com/nghiavtr/BPSC

Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis

https://research.cchmc.org/pbge/sincera.html

SC3 – consensus clustering of single-cell RNA-Seq data

http://biorxiv.org/content/early/2016/09/02/036558

Citrus: A toolkit for single cell sequencing analysis

http://biorxiv.org/content/early/2016/09/14/045070

Single-Cell Resolution of Temporal Gene Expression during Heart Development

http://www.cell.com/developmental-cell/fulltext/S1534-5807(16)30682-7

Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects

http://biorxiv.org/content/early/2016/11/15/087775

Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes

http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract

SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

http://biorxiv.org/content/early/2016/11/21/088856

SCOUP is a probabilistic model to analyze single-cell expression data during differentiation

https://github.com/hmatsu1226/SCOUP

scLVM is a modelling framework for single-cell RNA-seq data

https://github.com/PMBio/scLVM

Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories

https://github.com/jw156605/SLICER

SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality

http://www.morgridge.net/SinQC.html

TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis

https://github.com/zji90/TSCAN

Visualization and cellular hierarchy inference of single-cell data using SPADE

http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html

OEFinder: Identify ordering effect genes in single cell RNA-seq data

https://github.com/lengning/OEFinder

Delta: a new Web-based 3D genome visualization and analysis platform

Jit — Wed, 20 Dec 2017 08:49:55 -0600

Delta is an integrative visualization and analysis platform to facilitate visually annotating and exploring the 3D physical architecture of genomes. Delta takes Hi-C or ChIA-PET contact matrix as input and predicts the topologically associating domains and chromatin loops in the genome. It then generates a physical 3D model which represents the plausible consensus 3D structure of the genome. Deltafeatures a highly interactive visualization tool which enhances the integration of genome topology/physical structure with extensive genome annotation by juxtaposing the 3D model with diverse genomic assay outputs.

https://github.com/zhangzhwlab/delta

Address of the bookmark: https://github.com/zhangzhwlab/delta