BOL: Related items

Choosing the Right NGS Sequencing Instrument for Your Study

Neel — Wed, 15 Jun 2022 00:37:29 -0500

The right sequencing instrument for your study depends on your project goal. Setting aside turnaround time and price, it essentially comes down to the numbers of reads and read length you need for your experiment. Below, we've described and compared metrics for each of the instruments available. If you’re new to high-throughput sequencing and have questions about how you should design your sequencing run, fill out our free consultation form and we'll get in touch with you to help.

More at https://genohub.com/ngs-instrument-guide/

Address of the bookmark: https://genohub.com/ngs-instrument-guide/

Common steps for reads mapping !

BioStar — Thu, 09 Mar 2023 02:48:02 -0600

Mapping reads to a reference genome is an essential step in many types of genomic analysis, such as variant calling and gene expression analysis. Here are some general steps to follow for mapping reads to a genome:

Choose a read mapper: There are many read mappers available, such as BWA, Bowtie, and HISAT2. Choose a mapper that is appropriate for your type of data and research question.
Index the reference genome: Before mapping reads, the reference genome needs to be indexed. This involves creating an index of the genome sequence that allows the mapper to quickly find matches to the reads. Most mappers have their own indexing tools.
Prepare the read data: The reads should be in a format that is compatible with the mapper. Most mappers accept FASTQ or BAM files. Depending on the quality of the data, it may need to be filtered or trimmed before mapping.
Run the mapper: The mapper is run with the command-line interface or using a graphical user interface. The specific command depends on the mapper being used, but typically involves specifying the input data, reference genome, and output file format.
Evaluate the mapping results: After the mapping is complete, the results should be evaluated. This includes assessing the quality of the mapping, such as the mapping rate, the number of mapped reads, and the mapping quality score.
Post-processing: Depending on the analysis being performed, post-processing of the mapped reads may be necessary. This can include filtering reads based on quality, removing duplicate reads, and calling variants.

Overall, mapping reads to a reference genome is a complex process that requires careful consideration of the type of data, the research question, and the specific mapper being used.

NanoPack: visualizing and processing long-read sequencing data

Jit — Tue, 25 Dec 2018 21:20:50 -0600

The NanoPack tools are written in Python3 and released under the GNU GPL3.0 License. The source code can be found at https://github.com/wdecoster/nanopack, together with links to separate scripts and their documentation. The scripts are compatible with Linux, Mac OS and the MS Windows 10 subsystem for Linux and are available as a graphical user interface, a web service at http://nanoplot.bioinf.be and command line tools.

Address of the bookmark: https://github.com/wdecoster/nanopack

Understanding your reads and mapping !

Neel — Wed, 29 Jan 2020 06:29:55 -0600

One of the best tutorial for beginners ...

https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2017/Day1/Session4-seqIntro.html

Address of the bookmark: https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2017/Day1/Session4-seqIntro.html

BBSplit: Read Binning Tool for Metagenomes and Contaminated Libraries

Poonam Mahapatra — Wed, 03 Jan 2018 00:25:27 -0600

BBSplit internally uses BBMap to map reads to multiple genomes at once, and determine which genome they match best. This is different than with ordinary mapping. If a genome (say, human) contains an exact repeat somewhere, reads mapping to it will be mapped ambiguously. But if you want to determine whether reads are mouse or human, it does not matter whether they map ambiguously within human, only whether they are ambiguous between human and mouse. BBSplit tracks this additional ambiguity information and decides how to use it based on the “ambig2” flag. The normal use of BBSplit is like Seal, either quantifying how many reads go to each reference, or splitting the reads into multiple output files, one per reference. BBSplit can only be run using references indexed with BBSplit, as they contain additional information regarding which sequences came from which reference file.

BBSplit is a tool that bins reads by mapping to multiple references simultaneously, using BBMap. The reads go to the bin of the reference they map to best. There are also disambiguation options, such that reads that map to multiple references can be binned with all of them, none of them, one of them, or put in a special "ambiguous" file for each of them. Paired reads will always be kept together.

For example, if you had a library of something that was contaminated with e.coli and salmonella, you could do this:

bbsplit.sh in=reads.fq ref=ecoli.fa,salmonella.fa basename=out_%.fq outu=clean.fq int=t

This will produce 3 output files:
out_ecoli.fq (ecoli reads)
out_salmonella.fq (salmonella reads)
clean.fq (unmapped reads)

In this case, "int=t" means that the input file is paired and interleaved. For single-end reads you would leave that out. For paired reads in 2 files, you would do this:
bbsplit.sh in1=reads1.fq in2=reads2.fq ref=ecoli.fa,salmonella.fa basename=out_%.fq outu1=clean1.fq outu2=clean2.fq

BBSplit is available here:
https://sourceforge.net/projects/bbmap/

The sensitivity can be raised to be equivalent to BBMap with these flags: "minratio=0.56 minhits=1 maxindel=16000"

PEAR: a fast and accurate Illumina Paired-End reAd mergeR

Jit — Wed, 06 Apr 2016 13:27:23 -0500

PEAR is an ultrafast, memory-efficient and highly accurate pair-end read merger. It is fully parallelized and can run with as low as just a few kilobytes of memory.

PEAR evaluates all possible paired-end read overlaps and without requiring the target fragment size as input. In addition, it implements a statistical test for minimizing false-positive results. Together with a highly optimized implementation, it can merge millions of paired end reads within a couple of minutes on a standard desktop computer.

More at http://www.exelixis-lab.org/web/software/pear

Paper: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3933873/

Address of the bookmark: http://www.exelixis-lab.org/web/software/pear

SimLoRD: A read simulator for third generation sequencing reads

Aaryan Lokwani — Wed, 22 Aug 2018 10:40:27 -0500

SimLoRD is a read simulator for third generation sequencing reads and is currently focused on the Pacific Biosciences SMRT error model.

Reads are simulated from both strands of a provided or randomly generated reference sequence.

The reference can be read from a FASTA file or randomly generated with a given GC content. It can consist of several chromosomes, whose structure is respected when drawing reads. (Simulation of genome rearrangements may be incorporated at a later stage.)
The read lengths can be determined in four ways: drawing from a log-normal distribution (typical for genomic DNA), sampling from an existing FASTQ file (typical for RNA), sampling from a a text file with integers (RNA), or using a fixed length
Quality values and number of passes depend on fragment length.
Provided subread error probabilities are modified according to number of passes
Outputs reads in FASTQ format and alignments in SAM format

Address of the bookmark: https://bitbucket.org/genomeinformatics/simlord/

Alvis: a tool for contig and read ALignment VISualisation and chimera detection

LEGE — Wed, 08 May 2024 07:02:55 -0500

Alvis, a simple command line tool that can generate visualisations for a number of common alignment analysis tasks. Alvis is a fast and portable tool that accepts input in a variety of alignment formats and will output production ready vector images. Additionally, Alvis will highlight potentially chimeric reads or contigs, a common source of misassemblies.

More at https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04056-0

Address of the bookmark: https://github.com/SR-Martin/alvis

Five points for bioinformatics software/tools

Jitendra Narayan — Mon, 05 Aug 2013 04:12:32 -0500

In the bioinformatics sector we mostly spend time on computational analysis of huge amounts of data and try to make sense of it, biologically. But, most of the newbie bioinformaticians are faced with dilemma when they receive biological sequence data for the first time. They mostly found confusing over open source, user friendly GUI, and commercial bioinformatics software. Don’t be surprise this is true and also not an easy task to decide, because analytical step is the most crucial part and believe to be the biggest bottleneck in publishing paper in high impact journals. Through this blog I would like to address the pros and cons of both kind of software/tools and try to assist (Hmmm not really, It looks convince) you to make decision on your software selections.

The most common newbie questions are:

Should I try to use these free open source programs? Why are we not trying GUI software for computational analysis? Should I use commercial bioinformatics programs/software?”

1. Let’s be open

We generally think free and cheap are useless. But this concept is not applicable when we discuss open source software. Mostly, the bioinformatics software is developed by highly competitive biological programmers who believe in open sharing of knowledge. They come under Open Bioinformatics Foundation or O|B|F which is a non-profit, volunteer run organization focused on supporting open source programming in bioinformatics. The best part about open source tools/software is that they’re free to download the source code and read exactly what the program does. If you are so inclined, you can view all of the parts of the program and see the logical flow of the pipeline. In addition, open source makes an excellent learning tool for any beginning bioinformatician. Moreover, you can modify existing open source programs to deal with cutting-edge problems or to customize your pipeline. Apart from your computational and analysis work, most of the reviewer also prefers the open source based results so that they can validate the results if validation required.

2. Code headache

As a bioinformatician you are supposed to know the basics of programming languages, and if you are not good at it, then please learn it as soon as possible because you are not a bio-analyst but biological programmers. The open source programs usually lack dedicated service and support teams (often because they were the product of an overworked doc/postdoc!) so you are responsible for troubleshooting your own errors most of the time. We commonly receive the HELP email to support and assist to setup the pipeline; you can also find this kind of request on any QA forum. I personally believe this coding horror brings the biggest downside of open-source programs; where you need some programming skills in order to implement the program in your pipeline. But, if you are not able to fix the pipeline and modify the open source code according to your requirements them you should re-think on your bioinformatician name tag!!!

3. Dive into the codes

Some of the biologist turn bioinformatician says “if you can do the same thing with commercial software then why to get migraine with weird codes”, well this statement looks to me that guys are keen to learn swimming but still don’t like to get wet. If you are still using paid software and doing your work by customer support and clicking some of the well-designed GUI button then perhaps you are not interested in learning and trying new and challenging bioinformatics works. You are missing the basic flavour of bioinformatics. Let’s dive into the coding world, I am sure your will enjoy it. I recommend your to swim freely in code’s sea, and enjoy the journey; do not merely watch it from the outside.

4. Paid does not mean better

The bioinformatics company which are specializes in bioinformatics solutions develop well designed/packed, user friendly software by using a large number of specialised scientist, programmers and support staff. They also provide good services to accomplice your biological analysis work. This means that if you hit a ‘snag’ with your data, help is likely only a phone call away! These companies price their products competitively against the cost of a dedicated bioinformatician. You may be able to afford the program, but not the additional staff! Additionally, most of the functionality that you need in your analysis is already coded into the program. Need to plot a graph? Just click this button right here. It is that easy. But, as a bioinformatician this is not generally well encouraged approach in biological analysis work, because the software is not available to everyone and your data can’t be validated. Moreover, there is very less chances that anyone will repeat your work or love to do similar kind of research (because not all the labs in the world are rich like yours).

5. Take a caution

In biological analysis work, in which you deal GB/TB of data are having maximum chances of getting errors, so please be careful and always cross check your data before coming to any conclusion. Even an error in two line code can alter your entire analysis and display weird results. Some of the scientist blindly believes on commercial software, which is entirely wrong. Using proprietary tools does not absolve you of the need to actually read and research the type of analysis that you are doing. This is particularly true in the case of genome assembly and annotation.

At the end, I would like to tell only one think that open source solutions allows you to do more cutting edge analysis than the commercial tools. So let’s go for it.

Disclaimer:

This is my personal view. I have nothing to do with any company or open source community. The views expressed on these pages are mine alone and not those of my current/past employers. I do reserve the right to remove comments left by spammers or off-topic comments.

Linux for bioinformatician !!!

Rahul Nayak — Thu, 13 Mar 2014 16:59:26 -0500

Linux, free operating system for computers, provides several powerful admin tools and utilities which will help you to manage your systems effectively and handle huge amount of genomic/biological data with an ease. The field of bioinformatics relies heavily on Linux-based computers and software. Although most bioinformatics programs can be compiled to run. If you don’t know what these no so user-friendly tools are and how to use them, you could be spending lot of time trying to perform even the basic admin tasks. The focus of this linux series is to help you understand system admin as well as basic tools, which will help you to become an effective bioinformatician and computational biologist.

For knowledge about Linux and their importance amongst bioinformatician plesae read this article "An introduction to Linux for bioinformatics" by Paul Stothard.

Linux cheat sheet at http://bioinformaticsonline.com/file/view/87/linux-cheat-sheet

Please browse for futher useful linux pages on right hand side ...