BOL: Related items

QuorUM: An Error Corrector for Illumina Reads

Jit — Wed, 08 Nov 2017 11:40:41 -0600

Illumina Sequencing data can provide high coverage of a genome by relatively short (most often 100 bp to 150 bp) reads at a low cost. Even with low (advertised 1%) error rate, 100 × coverage Illumina data on average has an error in some read at every base in the genome. These errors make handling the data more complicated because they result in a large number of low-count erroneous k-mers in the reads. However, there is enough information in the reads to correct most of the sequencing errors, thus making subsequent use of the data (e.g. for mapping or assembly) easier. Here we use the term “error correction” to denote the reduction in errors due to both changes in individual bases and trimming of unusable sequence. We developed an error correction software called QuorUM. QuorUM is mainly aimed at error correcting Illumina reads for subsequent assembly. It is designed around the novel idea of minimizing the number of distinct erroneous k-mers in the output reads and preserving the most true k-mers, and we introduce a composite statistic π that measures how successful we are at achieving this dual goal. We evaluate the performance of QuorUM by correcting actual Illumina reads from genomes for which a reference assembly is available.

QuorUM is distributed as an independent software package and as a module of the MaSuRCA assembly software. Both are available under the GPL open source license at http://www.genome.umd.edu.

Address of the bookmark: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130821

QuorUM: An Error Corrector for Illumina Reads

BioStar — Tue, 04 Feb 2020 23:26:55 -0600

We produce trimmed and error-corrected reads that result in assemblies with longer contigs and fewer errors. We compared QuorUM against several published error correctors and found that it is the best performer in most metrics we use. QuorUM is efficiently implemented making use of current multi-core computing architectures and it is suitable for large data sets (1 billion bases checked and corrected per day per core)

Address of the bookmark: http://www.genome.umd.edu/

The "Ifs" and "Buts" of NGS Quality Control and Trimming

BioStar — Thu, 02 Jan 2025 20:11:07 -0600

Next-Generation Sequencing (NGS) has revolutionized biological research, providing vast amounts of data for a wide range of applications. However, the reliability of NGS analyses heavily depends on the quality of raw sequencing data. Quality control (QC) and trimming are critical preprocessing steps that can make or break your downstream analyses. In this blog, we explore the "ifs" (why you should perform QC and trimming) and the "buts" (challenges or considerations) of this vital step in NGS workflows.

The "Ifs" of NGS QC and Trimming

Ensures Data Integrity
If you want to minimize errors in downstream analyses, QC and trimming remove low-quality reads and bases, ensuring high-confidence data. This step is essential for reliable variant calling, assembly, and other applications.
Removes Contaminants
If adapter sequences or contaminants are present in the raw reads, trimming can eliminate them. This prevents issues like misalignment or incorrect biological interpretations, ensuring cleaner data for analysis.
Improves Mapping and Assembly
If your goal is better alignment to a reference genome or improved de novo assembly, trimming low-quality bases and adapters is critical. High-quality reads map more efficiently and generate more accurate assemblies.
Reduces Computational Load
If you want to save computational resources, trimming reduces the dataset size, which speeds up processing and analysis. Clean datasets mean less computational time spent on processing low-quality data.
Prepares for Standardized Analyses
If your project involves multiple datasets, QC and trimming ensure uniformity across them. This standardization makes comparisons valid and reproducible, particularly in large collaborative studies.

The "Buts" of NGS QC and Trimming

Risk of Over-Trimming
But excessive trimming can lead to the loss of informative sequences, reducing read depth and potentially discarding biologically relevant data. This is especially critical in studies with limited sequencing depth.
Bias Introduction
But trimming algorithms might introduce biases, especially if they inadvertently remove sequences with specific biological patterns. This can skew results and compromise biological insights.
Loss of Context in Paired-End Reads
But trimming one read in a pair more than the other can lead to loss of pairing information. This complicates downstream analyses that rely on paired-end data, such as structural variant detection.
Time and Resource Intensive
But running QC and trimming for large datasets can be computationally expensive and time-consuming. As sequencing depth increases, preprocessing becomes a bottleneck in the analysis pipeline.
Variable Standards
But the criteria for trimming (e.g., quality threshold, minimum read length) can vary between tools and datasets. This variability may affect reproducibility and comparability of results across studies.

Balancing the "Ifs" and "Buts"

To maximize the benefits of QC and trimming while mitigating the challenges, consider the following best practices:

Use QC Tools Wisely: Start with tools like FastQC to identify quality issues in your raw data. Visualizing quality metrics helps tailor your trimming parameters.
Choose Reliable Trimming Tools: Tools like Trimmomatic, Cutadapt, and BBduk offer adaptive and customizable trimming options. Select one that aligns with your dataset and project goals.
Set Reasonable Parameters: Avoid over-trimming by setting quality thresholds and minimum read lengths that balance data retention and quality improvement.
Test Downstream Effects: Validate the impact of QC and trimming on downstream analyses, such as alignment efficiency, variant calling accuracy, or assembly quality.
Document Your Workflow: Maintain detailed records of the parameters and tools used for QC and trimming. This ensures reproducibility and enables better troubleshooting.

Conclusion

NGS quality control and trimming are essential steps to ensure reliable and accurate data for analysis. While the "ifs" highlight the clear benefits of these steps, the "buts" remind us of the potential pitfalls. By adopting best practices and carefully balancing these considerations, you can optimize your preprocessing workflow and unlock the full potential of your sequencing data.

Bioinformatics approach to Boar Taint

Rahul Agarwal — Wed, 17 Jul 2013 15:50:37 -0500

Meat products obtained from intact male pigs often produce offensive smell or odour which is recognized as a complex genetic trait called boar taint.Androstenone and Skatole in the fat primarily cause boar taint. Metabolism of androstenone and sex steroids share a common pathway which makes removal of boar taint a very challenging task. Castration is a traditional solution to remove boar taint but it also results in bad quality of meat due to low level of steroids which is objectionable to many consumers. Detected functional variant(s) underlying boar taint compounds can be used as genetic markers in selection of male pigs with reduced boar taint levels. Resequencing of a total of 47 samples belong to Norwegian Landrace (NL) and Duroc (D) pigs with varied boar taint levels were done in Illumina HiSeq2000 to >10X average coverage. Short reads generated from these samples mapped to Sus Scrofa version 10.2 reference assembly using Bowtie2. Alignment file then used for calling SNPs and InDels inside previousy identified QTL regions on SSC5,13, and 7 with the aid of FreeBayes , a variant caller tool. A final list of SNPs was prepared after filtering SNPs on the basis of SNP quality, coverage of SNP allele, functional and structural annotation, and repeats, etc. Selected SNPs will be genotyped in sample population for validation and then used for constructing SNPs haplotypes in close linkage disequilibrium with QTLs and fine mapping of QTLs through association mapping of genotyped SNPs.

Genome Browsers

Rahul Agarwal — Fri, 16 Aug 2013 19:04:47 -0500

Genome Browser is the platform/database used for searching and retreiving sequences and annotation of genomes belong to various eukaryotes, prokaryotes, etc.

Following are the weblink for different available browsers:

http://www.ensembl.org/index.html

http://ensemblgenomes.org/

http://genome.ucsc.edu/

http://www.ncbi.nlm.nih.gov/genome

http://www.ebi.ac.uk/genomes/

http://flybase.org/

http://cmr.jcvi.org/tigr-scripts/CMR/CmrHomePage.cgi

http://www.sanger.ac.uk/resources/databases/

Latest paper on comparison of mapping tools

Rahul Agarwal — Tue, 03 Sep 2013 18:00:38 -0500

A. Hatem, D. Bozdag, A. E. Toland, U. V. Catalyurek "Benchmarking short sequence mapping tools" BMC Bioinformatics, 14(1):184, 2013.

http://bmi.osu.edu/hpc/software/benchmark/

http://bmi.osu.edu/hpc/software/pmap/pmap.html

Other similiar papers:

http://online.liebertpub.com/doi/pdf/10.1089/cmb.2012.0022

http://bioinformatics.oxfordjournals.org/content/28/24/3169

Some new Mapping tool links:

GSNAP

http://research-pub.gene.com/gmap/

RMAP

http://rulai.cshl.edu/rmap/

mrsFAST

http://mrsfast.sourceforge.net/Home

http://sourceforge.net/projects/mrsfast/files/mrsfast-ultra-3.1.0/

BFAST

http://sourceforge.net/apps/mediawiki/bfast/index.php?title=Main_Page

SHRiMP (for AB SOLiD color-space reads)

http://compbio.cs.toronto.edu/shrimp/

RazerA 3

http://www.seqan.de/projects/razers/

Address of the bookmark: http://www.biomedcentral.com/1471-2105/14/184

Largest Genome Sequenced

Rahul Agarwal — Fri, 21 Mar 2014 13:57:19 -0500

The enormous size of the loblolly pine genome having 22 billion base pairs compared to only 3 billion in the human genome. In other words, it is seven times larger than a human’s and also the largest and the most complete conifer genome ever sequenced.

Related Paper:

http://genomebiology.com/2014/15/3/R59/abstract

Address of the bookmark: http://www.news.ucdavis.edu/search/news_detail.lasso?id=10859

New RNA Seq tool

Rahul Agarwal — Fri, 25 Apr 2014 10:59:04 -0500

"By removing the time-consuming step of read mapping, the authors reported, Sailfish able to provide quantification estimates 20–30 times faster than current methods without loss of accuracy."

Tool link:

http://www.cs.cmu.edu/~ckingsf/software/sailfish/

Address of the bookmark: http://www.genengnews.com/gen-news-highlights/lightweight-algorithms-sail-through-rna-sequencing-data/81249765/

GenXPro GmbH

Rahul Agarwal — Thu, 22 May 2014 07:18:35 -0500

GenXPro GMbH is service provider for entire spectrum of nucleotide-based information of any biological sample. By combining intelligent data reduction techniques and latest next generation sequencing technologies, our service portfolio provides most accurate and cost efficient solutions for transcriptomic-, genomic- or epigenomic research.

GENXPRO GMBH, ALTENHÖFERALLEE 3, 60438 FRANKFURT MAIN, GERMANY

Website: http://www.genxpro.info/products_and_services/

PHONE: +49 (0)69- 95 73 97 10, FAX: +49 (0)69- 95 73 97 06

EMAIL: info@genxpro.de

Genomics and sequencing approach for identification of biomarkers to assess the efficacy of TGF-βRI inhibitors (of liver cancer) in vivo

Rahul Agarwal — Tue, 05 Aug 2014 13:55:32 -0500

Liver cancer is third leading cause of deaths and fourth most frequent occuring cancer worldwide. There are multiple signaling pathways responsible for causing cancer amongst which TGFb is most important cytokine whose signaling pathway promote cancer. However, main problem is to cure this cancer at late stage where we still have no treatment strategy to tackle this deadly cancer. Hence we need to find out new therapeutic target. One way is to look the relationships between mRNA, methylation and miRNA data of patients with different pathological conditions (cancer vs control either with inhibitor/not). MiRNA is small RNA molecules known to inhibit mRNA expression of particular gene by binding improperly to 3'UTR region of a gene and hence block binding of TF /translation of gene. CpG regions is known to located at promoter region of gene (5' UTR) and usually hypomethylated which allow to gene to transcribe and translate however sometime this region become hyper-methylated thats prevent expression of host gene. Thus , integration of these three data reveal new targets and pathways important for causing or preventing cancer and also reveal biomarker thats check the effects of inhibitor on signaling pathway underlying liver cancer.