<![CDATA[Last articles - STHDA : Bioinformatics]]> https://www.sthda.com (C) 2005-2026 PHPBoost en PHPBoost <![CDATA[Strategies for Analyzing Bisulfite Sequencing Data]]> https://www.sthda.com/english/articles/15-bioinformatics/69-strategies-for-analyzing-bisulfite-sequencing-data/ https://www.sthda.com/english/articles/15-bioinformatics/69-strategies-for-analyzing-bisulfite-sequencing-data/ DNA methylation, one of the main epigenetic modifications in the eukaryotic genome, has been shown to play a role in cell-type specific regulation of gene expression, and therefore cell-type identity.

Bisulfite sequencing is the gold-standard for measuring methylation over the genomes of interest, because it provides global coverage at single-base resolution.

The current article describes strategies for analyzing high-throughput bisulfite sequencing. The following steps are described:

- Short-read alignment techniques,
- pre/post-alignment quality check methods to ensure data quality,
- Subsequent analysis steps after alignment,
- Differential methylation methods
- Methylomes segmentation for identifying regulatory regions
- Annotation methods for further classification of regions returned by segmentation and differential methylation methods.

Finally, the article describes software packages and online workflow to efficiently handle large bisulfite sequencing datasets.

The content is organized as follow:

- Introduction to high-throughput sequencing techniques based on bisulfite treatment
- Algorithms and tools for detecting differential methylation and methylation profile segmentation.
- Management of large datasets and data analysis workflows with a guided user interface.


Bisulfite sequencing for detection of methylation and other base modifications




Whole genome bisulfite sequencing (WGBS) combines bisulfite conversion of DNA molecules with high-throughput sequencing.

The procedure can be summarized as follow is as follow:

- Random fragmentation of genomic DNA to the desired size (200pb)

- Conversion of the fragmented DNA into sequencing library by ligation to adaptors that contain 5mCs.

- Bisulfite treatment. This treatment effectively converts unmethylated cytosines to uracil while methylated cytosines remain protected.

- PCR amplification of the library (After the PCR, uracils will be represented as thymines)

- High-throughput sequencing.


Despite its advantages, WGBS remains the most expensive technique and standard library prep requires relatively large quantities of DNA (100ng&#8211;5 ug); as such, it is usually not applied to large numbers of samples. To achieve high sensitivity in detecting methylation differences between samples, high sequencing depth is required which leads to significant increase in sequencing cost.


Reduced representation bisulfite sequencing (RRBS) is another technique that can also profile DNA methylation at single-base resolution.It combines digestion of genomic DNA with restriction enzymes (MspI) and sequencing with bisulfite treatment in order to enrich for areas with high CpG content.

RRBS can sequence only CpG dense regions and doesn&#8217;t interrogate CpG-deficient regions such as functional enhancers, intronic regions, intergenic regions or in general lowly methylated regions (LMRs) of the genome. It has limited coverage of the genome in CpG-poor regions and examines about 4% to 17% of the approximately 28 million CpG dinucleotides distributed throughout the human genome depending on the sequencing depth and which variant of RRBS.


Workflow for analysis of DNA methylation using data from bisulfite sequencing experiments






Read more: Katarzyna Wreczycka et al., Strategies for analyzing bisulfite sequencing data, http://www.biorxiv.org/content/early/2017/08/09/109512
]]> Tue, 15 Aug 2017 10:35:00 +0200 <![CDATA[neoantigenR: An R Package for Identifying Tumor Neoantigen from Sequencing Data]]> https://www.sthda.com/english/articles/15-bioinformatics/62-neoantigenr-an-r-package-for-identifying-tumor-neoantigen-from-sequencing-data/ https://www.sthda.com/english/articles/15-bioinformatics/62-neoantigenr-an-r-package-for-identifying-tumor-neoantigen-from-sequencing-data/
neoantigenR: An annotation based pipeline for tumor neoantigen identification from sequencing data.

The authors introduce an R package that allows the discoveries of peptide epitope candidates, which are the tumor-specific peptide fragments containing potential functional neoantigens.

These peptide epitopes consist of structure variants including insertion, deletions, alternative sequences, and peptides from nonsynonymous mutations.

Analysis of these precursor candidates with widely used tools such as netMHC allows for the accurate in-silico prediction of neoantigens.

The pipeline named neoantigeR is currently hosted in https://github.com/ICBI/neoantigeR.

]]> Wed, 09 Aug 2017 22:22:00 +0200 <![CDATA[Identifying Actively Expressed Genes in RNA-seq Gene Expression Studies]]> https://www.sthda.com/english/articles/15-bioinformatics/61-identifying-actively-expressed-genes-in-rna-seq-gene-expression-studies/ https://www.sthda.com/english/articles/15-bioinformatics/61-identifying-actively-expressed-genes-in-rna-seq-gene-expression-studies/ Finding the active genes in deep RNA-seq gene expression studies".

The authors shows that human cell&#8217;s transcriptome can be divided into active and repressed genes.

They provide a novel normalization metric, zFPKM, that identifies the threshold between active and background gene expression; and they show that this threshold is robust to experimental and analytical variations.

The authors recommends using zFPKM > -3 to select expressed genes.

A Bioconductor R package, zFPKM (https://github.com/ronammar/zFPKM), is available for finding the actively expressed genes.]]> Wed, 09 Aug 2017 21:59:00 +0200 <![CDATA[Comparison of Computational Tools for Alternatively Spliced Transcript Isoforms Quantification]]> https://www.sthda.com/english/articles/15-bioinformatics/60-comparison-of-computational-tools-for-alternatively-spliced-transcript-isoforms-quantification/ https://www.sthda.com/english/articles/15-bioinformatics/60-comparison-of-computational-tools-for-alternatively-spliced-transcript-isoforms-quantification/
Evaluation and comparison of computational tools for RNA-seq isoform quantification.


A total of seven tools were compared: Cufflinks, RSEM, TIGAR2, eXpress, Sailfish[/b, [b]Kallisto and Salmon.

The authors, used RSEM simulated datasets to measure the accuracy of methods, technical replicates of experimental data to test the robustness, and simulated transcripts from the TP53 gene to illustrate the challenges of isoform quantification.

They found that alignment-free methods, such as Salmon, Sailfish and Kallisto, require less computational time while achieving similar or better accuracies compared with other methods.

Cufflinks and eXpress, two alignment-dependent algorithms, have inferior accuracy performance with an RSEM simulated dataset.

TIGAR2 has overall good performance, but the run time and memory requirements render the tool less popular for practical use.

Considering both the accuracy and computational resources needed, Salmonaln and RSEM are the two best performers among the alignment-dependent tools.
]]> Wed, 09 Aug 2017 17:44:00 +0200 <![CDATA[Unitas: Universal Tool to Annotate Small RNAs]]> https://www.sthda.com/english/articles/15-bioinformatics/27-unitas-universal-tool-to-annotate-small-rnas/ https://www.sthda.com/english/articles/15-bioinformatics/27-unitas-universal-tool-to-annotate-small-rnas/ Reliable annotation of small non-coding RNA data produced by high-throughput sequencing is time-consuming and requires robust bioinformatics expertise.

Here we present the unitas tool, for easily annotating small RNA sequence datasets. Unitas has support for more than 800 species referenced in Ensembl databases. It provides also numerous analysis features in a user-friendly manner.


General requirement



1) Internet connection

2) Operating system: Unitas is provided as a standalone executable compatible for Linux, MAC and Windows.

3) Perl: Unitas is written in Perl, which is commonly preinstalled on MAC and Linux; so, normally you don't need to install Perl on Mac and linux


Windows users that prefer to run the Perl script rather than the executable file may have to install a free Perl distribution such as ActivePerl or Strawberry Perl.


Unitas workflow



1) Downloading of a collection of latest reference sequences for subsequent mapping. Unitas connects to the Mainz University Server (MUS) to load the latest list of URLs for downloading the required reference sequence data.

2) Automated 3'adapter recognition and trimming

3) Filtering low complexity reads

4) Small RNA annotation: miRNA, ncRNA, piRNA, phasiRNA






Download



The unitas source code and precompiled executable files are freely available at http://www.smallrnagroup.uni-mainz.de/software.html.]]> Wed, 19 Jul 2017 14:15:00 +0200