BOL: Related items

Platanus

Jit — Fri, 13 May 2016 05:12:40 -0500

Platanus is a novel de novo sequence assembler that can reconstruct genomic sequences of
highly heterozygous diploids from massively parallel shotgun sequencing data.

The latest version is 1.2.4.

To cite Platanus, please use the following:

Kajitani R, Toshimoto K, Noguchi H, Toyoda A, Ogura Y, Okuno M, Yabana M, Harada M, Nagayasu E, Maruyama H, Kohara Y, Fujiyama A, Hayashi T, Itoh T, “Efficient de novo assembly of highly heterozygous genomes from whole-genome shotgun short reads”. Genome Res. 2014 Aug;24(8):1384-95. doi: 10.1101/gr.170720.113. [abstract | full text]

Address of the bookmark: http://platanus.bio.titech.ac.jp/

Flye: Fast and accurate de novo assembler for single molecule sequencing reads

BioJoker — Tue, 02 Apr 2019 21:54:55 -0500

Flye is a de novo assembler for single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. It is designed for a wide range of datasets, from small bacterial projects to large mammalian-scale assemblies. The package represents a complete pipeline: it takes raw PB / ONT reads as input and outputs polished contigs. Flye also includes a special mode for metagenome assembly.

Address of the bookmark: https://github.com/fenderglass/Flye

PAired-eND Assembler for DNA sequences

Neel — Wed, 06 Apr 2016 05:25:34 -0500

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

More at https://github.com/neufeld/pandaseq

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

ALE: a Generic Assembly Likelihood Evaluation Framework for Assessing the Accuracy of Genome and Metagenome Assemblies

Neel — Tue, 26 Apr 2016 03:38:43 -0500

Assembly Likelihood Evaluation (ALE) framework that overcomes these limitations, systematically evaluating the accuracy of an assembly in a reference-independent manner using rigorous statistical methods. This framework is comprehensive, and integrates read quality, mate pair orientation and insert length (for paired-end reads), sequencing coverage, read alignment and k-mer frequency. ALE pinpoints synthetic errors in both single and metagenomic assemblies, including single-base errors, insertions/deletions, genome rearrangements and chimeric assemblies presented in metagenomes. At the genome level with real-world data, ALE identifies three large misassemblies from the Spirochaeta smaragdinae finished genome, which were all independently validated by Pacific Biosciences sequencing. At the single-base level with Illumina data, ALE recovers 215 of 222 (97%) single nucleotide variants in a training set from a GC-rich Rhodobacter sphaeroides genome. Using real Pacific Biosciences data, ALE identifies 12 of 12 synthetic errors in a Lambda Phage genome, surpassing even Pacific Biosciences' own variant caller, EviCons. In summary, the ALE framework provides a comprehensive, reference-independent and statistically rigorous measure of single genome and metagenome assembly accuracy, which can be used to identify misassemblies or to optimize the assembly process.

More at http://www.ncbi.nlm.nih.gov/pubmed/23303509

Address of the bookmark: http://sc932.github.io/ALE/about.html

segemehl

Anjana — Tue, 10 May 2016 08:10:15 -0500

segemehl is a software to map short sequencer reads to reference genomes. Unlike other methods, segemehl is able to detect not only mismatches but also insertions and deletions. Furthermore, segemehl is not limited to a specific read length and is able to map primer- or polyadenylation contaminated reads correctly. segemehl implements a matching strategy based on enhanced suffix arrays (ESA).

More at http://www.bioinf.uni-leipzig.de/Software/segemehl/

Manual http://www.bioinf.uni-leipzig.de/Software/segemehl/segemehl_manual_0_1_7.pdf

Address of the bookmark: http://hoffmann.bioinf.uni-leipzig.de/LIFE/segemehl.html

WgSim

Jit — Thu, 23 Jun 2016 07:26:49 -0500

Reads simulator

Wgsim is a small tool for simulating sequence reads from a reference genome. It is able to simulate diploid genomes with SNPs and insertion/deletion (INDEL) polymorphisms, and simulate reads with uniform substitution sequencing errors. It does not generate INDEL sequencing errors, but this can be partly compensated by simulating INDEL polymorphisms.

Wgsim outputs the simulated polymorphisms, and writes the true read coordinates as well as the number of polymorphisms and sequencing errors in read names. One can evaluate the accuracy of a mapper or a SNP caller with wgsim_eval.pl that comes with the package.

Address of the bookmark: https://github.com/lh3/wgsim

NGS Tutorial

Jit — Mon, 05 Sep 2016 09:50:46 -0500

These tutorials are written for hundreds of bioinformaticians trying to cope with large volume of next-generation sequencing (NGS) data. NGS technologies brought a dramatic shift in the world of sequencing. Merely five years back, genome sequencing of higher eukaryotes used to be very expensive endeavor. To get a genome of interest sequenced, hundreds of scientists had to raise funds together by writing a joint white-paper and petitioning to various government agencies. The tasks of sequencing and assembly were handled by dedicated sequencing facilities, of which only a few existed around the globe. Naturally, the capacities at those sequencing facilities were significantly constrained from high volume of requests

Address of the bookmark: http://www.homolog.us/Tutorials/index.php

BASE: a practical de novo assembler for large genomes using long NGS reads

Rahul Nayak — Fri, 19 Oct 2018 07:25:21 -0500

new de novo assembler called BASE. It enhances the classic seed-extension approach by indexing the reads efficiently to generate adaptive seeds that have high probability to appear uniquely in the genome. Such seeds form the basis for BASE to build extension trees and then to use reverse validation to remove the branches based on read coverage and paired-end information, resulting in high-quality consensus sequences of reads sharing the seeds. Such consensus sequences are then extended to contigs.

Address of the bookmark: https://github.com/dhlbh/BASE

PHYMMBL

Jit — Mon, 10 Oct 2016 08:56:34 -0500

Metagenomics sequencing projects collect samples of DNA from uncharacterized environments that may contain hundreds or even thousands of species. One of the main challenges in analyzing a metagenome is phylogenetic classification of raw sequence reads into groups representing the same or similar species. Such classification is a useful prerequisite for genome assembly and for analysis of the biological diversity present in a sample. The newest sequencing technologies have simultaneously made metagenomics easier, by making the sequencing process faster, and more difficult, by producing shorter read lengths than previous technologies. Methods for classifying sequences as short as 100 base pairs (bp) have until now been relatively inaccurate, requiring metagenomics projects to use older, long-read technologies. Phymm, a new classification approach for metagenomics data which uses interpolated Markov models (IMMs) to taxonomically classify DNA sequences, can accurately classify reads as short as 100 bp. Its accuracy for short reads represents a significant leap forward over previous composition-based classification methods. PhymmBL (rhymes with "thimble"), the hybrid classifier included in this distribution which combines analysis from both Phymm and BLAST, produces even higher accuracy.

Address of the bookmark: http://www.cbcb.umd.edu/software/phymm/

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