BOL: Related items

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

Frequent parameters for bioinformatics tools !

BioStar — Tue, 27 Oct 2020 19:42:32 -0500

Third party executable parameters and options.

Trimmomatic

“ILLUMINACLIP:...:2:30:10”

“LEADING:15”

“TRAILING:15”

“SLIDINGWINDOW:4:20”

“MINLEN:20”

“TOPHRED33”

Filtlong

--min_length 500

--min_mean_q 85

--min_window_q 65

FastQ Screen

--aligner bowtie2' (bwa for PacBio)

--subset 1000 (for PacBio)

SPAdes

--careful

--disable-gzip-output

--cov-cutoff auto

--phred-offset 33

HGAP

Pbalign.task_options.min_accuracy: 70

Pbalign.task_options.no_split_subreads: false

Genomic_consensus.task_options.min_confidence: 40

falcon_ns.task_options.HGAP_GenomeLength_str:

6000000

Pbcoretools.task_options.read_length: 0

Genomic_consensus.task_options.use_score: 0

Pbalign.task_options.min_length: 50

Pbalign.task_options.algorithm_options: --minMatch 12

--bestn 10 --minPctSimilarity 70.0

Pbalign.task_options.hit_policy: randombest

Pbcoretools.task_options.other_filters: rq >= 0.7

Pbalign.task_options.concordant: false

Genomic_consensus.task_options.min_coverage: 5

falcon_ns.task_options.HGAP_SeedCoverage_str: 30

falcon_ns.task_options.HGAP_AggressiveAsm_bool: false

Genomic_consensus.task_options.algorithm: best

falcon_ns.task_options.HGAP_SeedLengthCutoff_str: -1

Genomic_consensus.task_options.diploid: false

MeDuSa

-random 100

Prokka

--usegenus

--force

--addgenes

--rfam

--rawproduct

cmsearch (taxonomy, 16S)

--rfam

--noali

blastn (taxonomy, 16S)

-evalue 1E-10

blastn (MLST)

-ungapped

-dust no

-evalue 1E-20

-word_size 32

-culling_limit 2

-perc_identity 95

blastp (VF)

-culling_limit 2

RGI (ABR)

--input_type contig

bowtie2 (mapping)

--sensitive

minimap2 (mapping)

-a

-x map-ont

samtools mpileup (SNP detection)

-uRI

bcftools call (SNP detection)

--variants-only

--skip-variants indels

--output-type v

--ploidy 1

-c

SNPsift filter (SNP detection)

"( QUAL >= 30 ) & (( na FILTER ) | (FILTER = 'PASS')) &

( DP >= 20 ) & ( MQ >= 20 )"

SNPeff ann (SNP detection)

-nodownload

-no-intron

-no-downstream

-no SPLICE_SITE_REGION

-upDownStreamLen 250

bcftools consensus

(phylogenetic tree)

--haplotype 1

fasttreemp

-nt

-boot 100

roary

-e

-n

-cd 100

-g 100000

Comparison of Short Read De Novo Alignment Algorithms

Rahul Agarwal — Wed, 21 Aug 2013 07:56:01 -0500

Excellent article to introduce different sequencing methods along with tools for de novo assembly of sequencing reads and their relevant references.

Title: Comparison of Short Read De Novo Alignment Algorithms

Author: Nikhil Gopal

Address of the bookmark: http://biochem218.stanford.edu/Projects%202011/Gopal%202011.pdf

3d-dna: 3D de novo assembly (3D DNA) pipeline

Jit — Thu, 28 Dec 2017 10:09:37 -0600

This code is designed to enable anyone to reproduce the Hs2-HiC and the AaegL4 genomes reported in: Dudchenko et al., De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science, 2017.

Unless otherwise noted, all terminology below is consistent with this paper, and all references to figures and tables in this readme refer to this paper. Specifically, some of the terminology used below is outlined in Figure S2. The assembly procedure is described in detail in the Supporting Online Materials, specifically in the section labelled “Pipeline description”.

In addition, the pipeline uses tools and methods from Juicer (Durand & Shamim et al., Cell Systems, 2016) and Juicebox (Durand & Robinson et al., Cell Systems, 2016), as well as additional dependencies noted below.

Feel free to post your questions and comments at: http://www.aidenlab.org/forum.html

http://aidenlab.org/documentation.html

Address of the bookmark: https://github.com/theaidenlab/3d-dna

ALPACA: A hybrid strategy for assembly of genomic DNA shotgun sequencing reads.

Seema Singh — Mon, 30 Apr 2018 04:38:40 -0500

ALPACA requires Celera Assembler 8.3 or later. It is recommended to build Celera Assembler from source. (Why? The pre-built binaries CA_8.3rc1 and CA8.3rc2 will work for any large data set.

Detail paper at https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-017-3927-8

Address of the bookmark: https://github.com/VicugnaPacos/ALPACA

PERGA: A Paired-End Read Guided De Novo Assembler for Extending Contigs Using SVM and Look Ahead Approach

Rahul Nayak — Tue, 05 Jun 2018 09:57:11 -0500

PERGA - Paired End Reads Guided Assembler PERGA is a novel sequence reads guided de novo assembly approach which adopts greedy-like prediction strategy for assembling reads to contigs and scaffolds. Instead of using single-end reads to construct contig, PERGA uses paired-end reads and different read overlap sizes from O ≥ Omax to Omin to resolve the gaps and branches. Moreover, by constructing a decision model using machine learning approach based on branch features, PERGA can determine the correct extension in 99.7% of cases. PERGA will try to extend the contigs by all feasible nucleotides and determine if these multiple extensions due to sequencing errors or repeats by using looking ahead technology, and it also try to separate the different repeats of nearby genomic regions to make the assembly result more longer and accurate. The simulated E.coli paired-end reads data are generated using GemSim (KE McElroy, F Luciani, T Thomas. Gemsim: General, Error-Model Based Simulator of Next-Generation Sequencing Data. BMC Genomics 2012, 13:74), with coverage 50x, 60x, 100x, read lengths 100-bp, and can be downloaded from https://github.com/zhuxiao/data_PERGA.

Address of the bookmark: https://github.com/hitbio/PERGA

ASplice: a scalable and memory-efficient algorithm for de novo transcriptome assembly

Rahul Nayak — Tue, 03 Jul 2018 04:09:46 -0500

With increased availability of de novo assembly algorithms, it is feasible to study entire transcriptomes of non-model organisms. While algorithms are available that are specifically designed for performing transcriptome assembly from high-throughput sequencing data, they are very memory-intensive, limiting their applications to small data sets with few libraries. Texas A&M University researchers develop a transcriptome assembly algorithm that recovers alternatively spliced isoforms and expression levels while utilizing as many RNA-Seq libraries as possible that contain hundreds of gigabases of data. New techniques are developed so that computations can be performed on a computing cluster with moderate amount of physical memory. Availability – A software program that implements the algorithm is available at: http://faculty.cse.tamu.edu/shsze/asplice. Sze SH, Pimsler ML, Tomberlin JK, Jones CD, Tarone AM. (2017) A scalable and memory-efficient algorithm for de novo transcriptome assembly of non-model organisms. BMC Genomics 18(Suppl 4):387.

Address of the bookmark: http://faculty.cse.tamu.edu/shsze/asplice/

MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph

Jit — Wed, 14 Nov 2018 04:50:27 -0600

MEGAHIT is a single node assembler for large and complex metagenomics NGS reads, such as soil. It makes use of succinct de Bruijn graph (SdBG) to achieve low memory assembly. MEGAHIT can optionally utilize a CUDA-enabled GPU to accelerate its SdBG contstruction. The GPU-accelerated version of MEGAHIT has been tested on NVIDIA GTX680 (4G memory) and Tesla K40c (12G memory) with CUDA 5.5, 6.0 and 6.5. MEGAHIT v1.0 or greater also supports IBM Power PC and has been tested on IBM POWER8.

https://academic.oup.com/bioinformatics/article/31/10/1674/177884

Address of the bookmark: https://github.com/voutcn/megahit

StringTie Transcript assembly and quantification for RNA-Seq

Jit — Tue, 09 Jun 2020 05:21:11 -0500

StringTie is a fast and highly efficient assembler of RNA-Seq alignments into potential transcripts. It uses a novel network flow algorithm as well as an optional de novo assembly step to assemble and quantitate full-length transcripts representing multiple splice variants for each gene locus. Its input can include not only alignments of short reads that can also be used by other transcript assemblers, but also alignments of longer sequences that have been assembled from those reads. In order to identify differentially expressed genes between experiments, StringTie's output can be processed by specialized software like Ballgown, Cuffdiff or other programs (DESeq2, edgeR, etc.).

Address of the bookmark: https://ccb.jhu.edu/software/stringtie/