BOL: Related items

RNA-Seq Analysis: A Guide for Bioinformaticians

LEGE — Sat, 07 Dec 2024 22:22:24 -0600

RNA sequencing (RNA-Seq) has revolutionized transcriptomics, offering unprecedented insights into gene expression, splicing, and transcript diversity. For bioinformaticians, RNA-Seq analysis is a gateway to exploring the complexity of RNA biology and its implications in health and disease. This blog post provides an overview of RNA-Seq analysis, key computational steps, and tools for bioinformaticians eager to delve into this powerful technique.

What is RNA-Seq?

RNA-Seq is a next-generation sequencing (NGS) technology used to study the transcriptome—the complete set of RNA molecules in a cell. It quantifies gene expression, detects novel transcripts, and captures alternative splicing events with high sensitivity and resolution.

Workflow for RNA-Seq Analysis

RNA-Seq analysis involves several stages, each requiring computational tools and expertise.

1. Experimental Design and Data Acquisition

Before diving into analysis, bioinformaticians should consider:

Biological Replicates: Ensure statistical power to detect meaningful differences.
Sequencing Depth: Align sequencing depth to study objectives (e.g., higher depth for low-abundance transcripts).
Paired-End vs. Single-End: Paired-end sequencing provides more detailed information on transcript structure.

Once sequencing is complete, raw data is provided in FASTQ format, containing sequence reads and quality scores.

2. Quality Control and Preprocessing

Quality control (QC) ensures data integrity. Tools such as FastQC evaluate metrics like base quality, GC content, and adapter contamination.

Preprocessing Steps:

Trimming: Tools like Trimmomatic or Cutadapt remove low-quality bases and adapter sequences.
Filtering: Discard reads below a certain quality threshold or length.

3. Read Alignment

Reads are mapped to a reference genome or transcriptome to determine their origin. Alignment tools include:

HISAT2: Handles large genomes efficiently and supports spliced alignments.
STAR: High-speed aligner optimized for RNA-Seq.
Bowtie2: Suitable for short-read alignment.

Output: A SAM/BAM file containing aligned reads.

4. Transcript Assembly and Quantification

This step involves identifying transcripts and quantifying their expression levels. Tools used include:

StringTie: Assembles and quantifies transcripts from aligned reads.
Salmon/Kallisto: Perform pseudo-alignment for rapid and accurate quantification.

Expression levels are typically measured as TPM (transcripts per million) or FPKM (fragments per kilobase of transcript per million mapped reads).

5. Differential Expression Analysis

To identify genes with altered expression between conditions, bioinformaticians use tools such as:

DESeq2: Accounts for data normalization and variability.
edgeR: Handles overdispersed count data efficiently.
Limma-voom: Combines linear modeling with RNA-Seq count data.

The output includes a list of differentially expressed genes (DEGs) with statistical significance and fold-change values.

6. Functional Annotation and Pathway Analysis

Understanding the biological significance of DEGs involves:

Gene Ontology (GO) Analysis: Tools like DAVID or clusterProfiler categorize genes based on their biological functions.
Pathway Enrichment Analysis: Identifies pathways enriched in DEGs using tools like KEGG, Reactome, or GSEA.

7. Visualization

Visualizing results enhances interpretability. Common visualizations include:

Heatmaps: Show expression patterns across samples (e.g., pheatmap).
Volcano Plots: Highlight significant DEGs (e.g., ggplot2).
PCA/UMAP: Assess sample clustering and variability (e.g., Seurat).

Challenges in RNA-Seq Analysis

Batch Effects: Technical variability can confound biological signals. Combat this with normalization techniques or batch-correction tools like ComBat.
Low-Quality Samples: Poor-quality RNA impacts downstream analyses.
Computational Complexity: RNA-Seq generates massive datasets, requiring robust computing resources and optimized pipelines.

Key Tools and Resources

Bioconductor: A treasure trove of R packages for RNA-Seq analysis.
Galaxy: A web-based platform for running RNA-Seq workflows.
Nextflow/Snakemake: Workflow management tools to streamline analyses.

Applications of RNA-Seq

RNA-Seq is used in diverse research areas, including:

Cancer Transcriptomics: Identifying tumor-specific expression profiles.
Developmental Biology: Studying dynamic transcriptome changes.
Drug Discovery: Screening genes modulated by therapeutic compounds.

Conclusion

RNA-Seq analysis is a cornerstone of modern transcriptomics, offering bioinformaticians a versatile toolkit for unraveling gene expression and regulation. Mastering RNA-Seq workflows and tools empowers researchers to transform raw sequencing data into biological discoveries.

Whether you’re investigating disease mechanisms, exploring cellular pathways, or developing new therapeutics, RNA-Seq is a powerful ally in your bioinformatics arsenal.

Bioinformatics tools developed for Oxford Nanopore data analysis !

biogeek — Wed, 27 Dec 2017 20:47:30 -0600

MinION is the only portable real-time device for DNA and RNA sequencing. Each consumable flow cell can now generate 10–20 Gb of DNA sequence data. Ultra-long read lengths are possible (hundreds of kb) as you can choose your fragment length. One of the technical advantages of ONT data is the read length, which offers great prospects for genome assembly. Generally, assemblers are based on several different types of algorithms, such as greedy, overlap-layout-consensus (OLC), de Bruijn graph (DBG), and string graph.

List of analysis tools developed for Oxford Nanopore data

BWA
Fast nanopore data tuned alignment tool
https://github.com/lh3/bwa

GraphMap
Mapper for long and error-prone reads
https://github.com/isovic/graphmap

LAST
Nanopore tuned alignment tool
http://last.cbrc.jp/

LINKS
Software tool for long read scaffolding
https://github.com/warrenlr/LINKS/

marginAlign
Tools to align nanopore reads to a reference
https://github.com/benedictpaten/marginAlign

minoTour
Real time analysis tools
http://minotour.nottingham.ac.uk/

nanoCORR
Error-correction tool for nanopore sequence data
https://github.com/jgurtowski/nanocorr

NanoOK
Software for nanopore data, quality and error profiles
https://documentation.tgac.ac.uk/display/NANOOK/NanoOK

Nanopolish
Nanopore analysis and genome assembly software
https://github.com/jts/nanopolish

nanopore
Variant-detection tool for nanopore sequence data
https://github.com/mitenjain/nanopore

Nanocorrect
Error-correction tool for nanopore sequence data
https://github.com/jts/nanocorrect/

npReader
Real-time conversion and analysis of nanopore reads
https://github.com/mdcao/npReader

poRe
Tool for analyzing and visualizing nanopore data
https://sourceforge.net/p/rpore/wiki/Home/

PoreSeq
Error-correction and variant-calling software
https://github.com/tszalay/poreseq

Poretools
Nanopore sequence analysis and visualization software
https://github.com/arq5x/poretools

SSPACE-LongRead
Genome scaffolding tool
http://www.baseclear.com/genomics/bioinformatics/basetools/SSPACE-longread

SMIS
Genome scaffolding tool
https://sourceforge.net/projects/phusion2/files/smis/

List of assemblers for Oxford Nanopore MinION long reads

LQS
DALIGNER, Celera OLC Nanocorrect,
Nanopolish corrector
https://github.com/jts/nanopolish

PBcR
HGAP or BLASR, Celera OLC
PBcR corrector
http://wgs-assembler.sourceforge.net/wiki/index.php/PBcR
–
Canu
MHAP, Celera OLC
Canu corrector
https://github.com/marbl/canu

Falcon
String graph, Celera OLC
Falcon corrector
https://github.com/PacificBiosciences/falcon

Miniasm
OLC
https://github.com/lh3/miniasm

ra-integrate
OLC
https://github.com/mariokostelac/ra-integrate/

ALLPATHS-LG
de Bruijn graph
ALLPATHS-L corrector
https://www.broadinstitute.org/software/allpaths-lg/blog/?page_id=12

SPAdes
de Bruijn graph
SPAdes corrector
http://bioinf.spbau.ru/spades

Research Associate Bioinformatics in IISc Recruitment 2020

Tue, 23 Jun 2020 21:53:34 -0500

Research Associate Bioinformatics in IISc Recruitment 2020

Essential Qualifications: Ph.D. (Bioinformatics/ Biophysics/ Biotechnology or any other stream of biological/ physical sciences) with a minimum of two publications in reputed peer reviewed journals in the area of structural bioinformatics or biophysics or biomolecular modeling/ simulation.

Job description: Development of bioinformatics tools and algorithms/software for structure based analysis of biomolecular systems. Programmatic access to major biomolecular databases using APIs Knowledge based prediction and analysis of biomolecular structure, function and interactions. Docking/simulations for inhibitor design.

Desirable Qualifications (Research Associate/s): i) Strong computer programming skills (in Python/PERL/PHP or C++ or object oriented database management systems like MySQL etc or scripting languages under LINUX/UNIX environment).

ii) Extensive experience in computational analysis of biomolecular structure/interactions and usage of advanced biomolecular simulation softwares. iii) Adequate knowledge of major databases, webservers and softwares in the area of biomolecular structure/function and drug design. iv) Familiarity with Parallel Programming environments and experience in usage of high-end HPC clusters.

The candidates must highlight their experience in above mentioned fields/topics in their CV. Initial appointment will be for a period of 1 year, subject to extension after review of performance.

Emoluments: As per DST, GOI norms and commensurate with experience.

More at https://www.iisc.ac.in/positions-open/

Troyanskaya Lab

Tue, 04 Feb 2020 06:40:36 -0600

The goal of our research is to interpret and distill this complexity through accurate analysis and modeling of molecular pathways, particularly those in which malfunctions lead to the manifestation of disease. We are inventing integrative methods for systems-level pathway modeling through integrative analysis of genome-scale datasets. We apply these approaches in studying challenging biological problems, such as how pathways function in diverse cell types and how they change dynamically.

https://function.princeton.edu/

SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

BioStar — Mon, 17 Aug 2020 05:21:09 -0500

SHAMAN is a shiny application for differential analysis of metagenomic data (16S, 18S, 23S, 28S, ITS and WGS) including bioinformatics treatment of raw reads for targeted metagenomics, statistical analysis and results visualization with a large variety of plots (barplot, boxplot, heatmap, …).
The bioinformatics treatment is based on Vsearch [Rognes 2016] which showed to be both accurate and fast [Wescott 2015].The statistical analysis is based on DESeq2 R package [Anders and Huber 2010] which robustly identifies the differential abundant features as suggested in [McMurdie and Holmes 2014] and [Jonsson2016]. SHAMAN robustly identifies the differential abundant genera with the Generalized Linear Model implemented in DESeq2 [Love 2014].
SHAMAN is compatible with standard formats for metagenomic analysis (.csv, .tsv, .biom) and figures can be downloaded in several formats. A presentation about SHAMAN is available here and a poster here.

More at https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03666-4

Address of the bookmark: https://github.com/aghozlane/shaman

Introduction to Bioinformatics and Computational Biology

Jit — Mon, 25 Jan 2021 01:32:30 -0600

This is the course material for STAT115/215 BIO/BST282 at Harvard University.

Xiaole Shirley Liu (lead instructor)
Joshua Starmer
Martin Hemberg
Ting Wang
Feng Yue

Ming Tang
Yang Liu
Jack Kang
Scarlett Ge
Jiazhen Rong
Phillip Nicol
Maartin De Vries

We thank many colleagues in the community, who helped Dr. Liu in prepare the STAT115/215 BIO/BST282 course over the years.

Address of the bookmark: https://liulab-dfci.github.io/bioinfo-combio/

Bioinformatics Head (Bioinformatics Manager III), Cancer Genomics Research Laboratory at Frederick National Laboratory

Wed, 18 Aug 2021 00:19:48 -0500

Frederick National Laboratory seeking an enthusiastic, creative, and seasoned bioinformatics professional to join our leadership team and direct the exceptional Bioinformatics Group at the Cancer Genomics Research Laboratory (CGR). CGR has a diverse team of bioinformatics and computational scientists that support all areas of bioinformatics and data analysis (infrastructure, data QC, pipeline development and maintenance, data curation and sharing, methodology development, statistical analyses, machine learning approaches, and scientific interpretation).

More at https://leidosbiomed.csod.com/ats/careersite/jobdetails.aspx?site=4&c=leidosbiomed&id=2040

Kanthida Lab !

Wed, 28 Apr 2021 02:27:22 -0500

Research Interest:

Bioinformatics

High-throughput and high-dimensional data analysis

Microbiome data analysis (Main focus)

Next-generation and third-generation sequencing data analysis for genomics

Gene expression data analysis

Machine learning for biological data

Biomarkers identification

Database and web-application for biological data

More at
https://sites.google.com/mail.kmutt.ac.th/kanthida-k/home?authuser=0

Researcher in computer science/biology

Mon, 15 Jul 2013 18:38:40 -0500

Researcher in Computer Science at the Computational Biology Unit - temporary employment

The Department of Informatics is a vacant position as a researcher in computer science, related to Computational Biology Unit (CBU), for 3 years.

The position is part of CBU Service Group and will focus on bioinformatic analysis project and especially the analysis of high-throughput data, including NGS (sequencing), and proteomics data.

The successful candidate will be part of the Norwegian bioinformatics platform's national helpdesk within the project ELIXIR.NO

Applicants must hold a PhD in a relevant subject such as computer science, mathematics, molecular biology and also possess expertise and experience in bioinformatics statistics and analysis of data from high-throughput molecular experiment.

Basic programming or scripting skills are required. Experience in Python, R, Perl, Linux-based operating systems and moreover knowledge of databases and web programming will be a strength for applicants.

We expect enthusiasm and independence and moreover the ability to work in an interdisciplinary team environment.

Good knowledge of English is required.

Salaries start at level 57 (code 1109/LR 24.1) by appointment. Further promotion occurs after
service seniority in the position (at grade 57-65). Of particularly highly qualified applicants may be considered a higher salary.

Further information about the position is available from the chair of the CBU,
Professor Inge Jonassen, e-mail: Inge.Jonassen @ ii.uib.no

The successful applicant must comply with the guidelines that apply at any given time the position.

State employment shall as far as possible reflect the diversity of the population. It is therefore an objective to achieve a balanced age and sex composition and the recruitment of persons with immigrant backgrounds. Persons with immigrant background are requested to apply for the position.

Women are particularly encouraged to apply. If the experts find that several applicants have approximately equivalent qualifications, the rules on equal in the Personnel Regulations for Academic Positions will be applied.

University of Bergen applies the principles of public openness when recruiting staff to scientific positions.

Information about the applicant may be made public even though the applicant has requested not to be named in the list of applicants. If the request does not host admitted to the result, the applicant shall be notified of this.

Send application, CV, certificates, diplomas, undergraduate work and a list of publications (list of publications) online by clicking on https://www.jobbnorge.no/jobbsoknet/login.aspx?returnurl=/jobbsoknet/jobapplication.aspx?jobid=95196

You need to upload certified translations into English or a Scandinavian language of appendices, such as diplomas and transcripts.

Applications sent by email to individuals at the institute will not be considered.

Deadline: 9 August 2013

A Beginner's Guide to Using Kraken for Taxonomic Classification

Neel — Fri, 13 Dec 2024 11:29:03 -0600

Kraken is a popular bioinformatics tool designed for fast and accurate taxonomic classification of metagenomic sequences. Its efficiency and precision make it a go-to resource for analyzing microbial communities, including bacteria, viruses, archaea, and fungi. Whether you're new to bioinformatics or experienced in the field, Kraken is an indispensable tool for taxonomic analysis.

In this blog, we’ll walk through the basics of Kraken, from installation to running an analysis, and highlight its key features and applications.

What is Kraken?

Kraken is a sequence classification tool that assigns taxonomic labels to DNA sequences using exact k-mer matching. It uses a reference database of genomes, dividing sequences into k-mers and identifying matches in a computationally efficient way.

Key Features of Kraken

Speed: Kraken processes data much faster than alignment-based methods.
Accuracy: It uses a precise k-mer matching algorithm for high-resolution taxonomic assignments.
Scalability: It can handle large metagenomic datasets.
Custom Databases: You can build and use custom databases tailored to your research needs.

Installing Kraken

System Requirements
- A Unix-based operating system (Linux/macOS).
- Sufficient computational resources for database building (RAM and disk space).
Installation Steps
- Clone the Kraken repository from GitHub:
  
  git clone https://github.com/DerrickWood/kraken.git cd kraken
- Compile the Kraken binaries:
  
  make
- Add Kraken to your PATH for easy access:
  
  export PATH=$PATH:/path/to/kraken

Preparing a Database

Kraken requires a database of reference genomes. You can use a pre-built database or create a custom one.

Downloading a Pre-built Database
Kraken offers pre-built databases, such as the MiniKraken database, which is lightweight and suitable for smaller datasets. Download it using:

kraken-build --download-library minikraken
Building a Custom Database
To include specific genomes, download FASTA files and build the database:

kraken-build --download-library bacteria --threads 4 --db my_database kraken-build --build --db my_database

This process may take considerable time and resources, depending on the size of the database.

Running Kraken

Once the database is ready, you can classify sequences.

Basic Usage
Use the following command to classify sequences:

kraken --db my_database --threads 4 --fastq-input input_sequences.fastq --output kraken_output.txt

Key options:
- --db: Specifies the database.
- --threads: Number of threads for parallel processing.
- --fastq-input: Indicates input file format (FASTQ/FASTA).
Interpreting Results
Kraken generates an output file with columns for sequence IDs, taxonomic classifications, and the confidence score.

Visualizing Kraken Results

Kraken results can be visualized using tools like Krona or converted to human-readable reports using kraken-report.

Generate a Report

kraken-report --db my_database kraken_output.txt > kraken_report.txt
Krona Visualization
Install Krona and convert Kraken output for visualization:

cut -f2,3 kraken_output.txt | ktImportTaxonomy -o krona_output.html

Open the HTML file in your browser to interactively explore the taxonomic classifications.

Advanced Usage

Confidence Thresholds
Adjust the confidence threshold for classification using the --confidence option. Higher values reduce false positives but may miss some true positives:

kraken --db my_database --confidence 0.1 --fastq-input input.fastq
Paired-End Reads
For paired-end sequencing data, use:

kraken --db my_database --paired reads_1.fastq reads_2.fastq
Customizing K-mers
Kraken allows you to set custom k-mer lengths during database building for specific applications.

Applications of Kraken

Microbial Ecology: Characterizing microbial communities in soil, water, and the human microbiome.
Pathogen Detection: Identifying pathogens in clinical samples.
Fungal Research: Analyzing fungal diversity in metagenomic datasets.
Environmental Monitoring: Tracking microbial populations in diverse habitats.

Conclusion

Kraken is a versatile and efficient tool for taxonomic classification in metagenomics. Its speed, accuracy, and flexibility make it a favorite among bioinformaticians. By following this guide, you can set up and use Kraken to unlock insights into microbial and fungal communities, paving the way for discoveries in ecology, medicine, and biotechnology.