BOL: Related items

How to sequence the human genome - Mark J. Kiel

Fri, 30 May 2014 13:24:11 -0500

View full lesson: http://ed.ted.com/lessons/how-to-sequence-the-human-genome-mark-j-kiel Your genome, every human's genome, consists of a unique DNA sequence of A's, T's, C's and G's that tell your cells how to operate. Thanks to technological advances, scientists are now able to know the sequence of letters that makes up an individual genome relatively quickly and inexpensively. Mark J. Kiel takes an in-depth look at the science behind the sequence. Lesson by Mark J. Kiel, animation by Marc Christoforidis.

Stampy

Abhi — Fri, 20 May 2016 19:13:32 -0500

Stampy is a package for the mapping of short reads from illumina sequencing machines onto a reference genome. It's recommended for most workflows, including those for genomic resequencing, RNA-Seq and Chip-seq. Stampy excels in the mapping of reads containing that contain sequence variation relative to the reference, in particular for those containing insertions or deletions.

Address of the bookmark: http://www.well.ox.ac.uk/project-stampy

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.

LoRDEC: a hybrid error correction program for long, PacBio reads

Jit — Mon, 10 Apr 2017 04:16:09 -0500

LoRDEC is a program to correct sequencing errors in long reads from 3rd generation sequencing with high error rate, and is especially intended for PacBio reads. It uses a hybrid strategy, meaning that it uses two sets of reads: the reference read set, whose error rate is assumed to be small, and the PacBio read set, which is then corrected using the reference set. Typically, the reference set contains Illumina reads.

Usually, errors in PacBio reads include many insertions and deletions, and comparatively less substitutions. LoRDEC can correct errors of all these types.
After correction, a larger portion of the sequence of PacBio reads is usable for detection of region of similarity with other sequences, for aligning them to the contigs of an assembly, etc.

Why is LoRDEC different?

It is efficient and can process large read data sets, included from eukaryotic or vertebrate species, on a usual computing server, and even works on desktop/laptop computers.
It adopts a novel graph based approach: it builds a succinct De Bruijn Graph (DBG) representing the short reads, and seeks a corrective sequence for each erroneous region of a long read by traversing chosen paths in the graph.

Address of the bookmark: http://www.atgc-montpellier.fr/lordec/

PANDASEQ is a program to align Illumina reads, optionally with PCR primers embedded in the sequence, and reconstruct an overlapping sequence.

BioStar — Fri, 21 Sep 2018 10:19:52 -0500

Development packages for zlib and libbz2 are needed, as well as a standard compiler environment. On Ubuntu, this can be installed via:

sudo apt-get install build-essential libtool automake zlib1g-dev libbz2-dev pkg-config

On MacOS, the Apple Developer tools and Fink (or MacPorts or Brew) must be installed, then:

sudo fink install bzip2-dev pkgconfig

Address of the bookmark: https://github.com/neufeld/pandaseq

INC-Seq: accurate single molecule reads using nanopore sequencing

Jit — Mon, 27 Nov 2017 10:38:56 -0600

INC-Seq reads enabled accurate species-level classification, identification of species at 0.1 % abundance and robust quantification of relative abundances, providing a cheap and effective approach for pathogen detection and microbiome profiling on the MinION system.

Address of the bookmark: https://github.com/CSB5/INC-Seq

npScarf: real-time scaffolder using SPAdes contigs and Nanopore sequencing reads

Shruti Paniwala — Mon, 11 Jun 2018 05:14:57 -0500

npScarf (jsa.np.npscarf) is a program that connect contigs from a draft genomes to generate sequences that are closer to finish. These pipelines can run on a single laptop for microbial datasets. In real-time mode, it can be integrated with simple structural analyses such as gene ordering, plasmid forming.

Address of the bookmark: http://japsa.readthedocs.io/en/latest/tools/jsa.np.npscarf.html

LoRMA: A tool for correcting sequencing errors in long reads

Abhimanyu Singh — Thu, 06 Sep 2018 16:21:01 -0500

An error correction method that uses long reads only. The method consists of two phases: first, we use an iterative alignment-free correction method based on de Bruijn graphs with increasing length of k-mers, and second, the corrected reads are further polished using long-distance dependencies that are found using multiple alignments. According to our experiments, the proposed method is the most accurate one relying on long reads only for read sets with high coverage. Furthermore, when the coverage of the read set is at least 75×, the throughput of the new method is at least 20% higher.

conda install -c atgc-montpellier lorma

Address of the bookmark: https://gite.lirmm.fr/lorma/lorma-releases/wikis/home

Short-read assembly using Spades !

Abhimanyu Singh — Mon, 31 Jan 2022 07:18:16 -0600

If we only had Illumina reads, we could also assemble these using the tool Spades.

You can try this here, or try it later on your own data.

Get data

We will use the same Illumina data as we used above:

illumina_R1.fastq.gz: the Illumina forward reads
illumina_R2.fastq.gz: the Illumina reverse reads

Assemble

Run Spades:

spades.py -1 illumina_R1.fastq.gz -2 illumina_R2.fastq.gz --careful --cov-cutoff auto -o spades_assembly_all_illumina

-1 is input file of forward reads
-2 is input file of reverse reads
--careful minimizes mismatches and short indels
--cov-cutoff auto computes the coverage threshold (rather than the default setting, “off”)
-o is the output directory

Results

Move into the output directory and look at the contigs:

infoseq contigs.fasta

CovCal: Coverage / Read Count Calculator

Jit — Wed, 15 Jun 2016 18:08:13 -0500

Coverage / Read Count Calculator

Calculate how much sequencing you need to hit a target depth of coverage (or vice versa).

Instructions: set the read length/configuration and genome size, then select what you want to calculate.

Written by Stephen Turner, based on the Lander-Waterman formula, inspired by a similar calculator written by James Hadfield. Coverage is calculated as C=LN/G and reads as N=CG/L where C = Coverage (X),L = Read length (bp), G = Haploid genome size (bp), and N = Number of reads. Source code on GitHub.

Address of the bookmark: http://apps.bioconnector.virginia.edu/covcalc/