# plumber.R

#* Echo back the input
#* @param msg The message to echo
#* @get /echo
function(msg=""){
  list(msg = paste0("The message is: '", msg, "'"))
}

#* Plot a histogram
#* @png
#* @get /plot
function(){
  rand <- rnorm(100)
  hist(rand)
}

#* Return the sum of two numbers
#* @param a The first number to add
#* @param b The second number to add
#* @post /sum
function(a, b){
  as.numeric(a) + as.numeric(b)
}

Address of the bookmark: https://www.rplumber.io/

CSAR-web can serve as a convenient and useful scaffolding tool allowing the users to efficiently and accurately scaffold their draft genomes according to a complete or incomplete reference genome. 

Address of the bookmark: http://genome.cs.nthu.edu.tw/CSAR-web

https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkw377

Address of the bookmark: http://amp.pharm.mssm.edu/Enrichr/

CGHub
https://cghub.ucsc.edu/
Comprehensive data repository; huge data size

EGA
https://www.ebi.ac.uk/ega/
Comprehensive data repository; huge data size

COSMIC
http://cancer.sanger.ac.uk
Largest somatic mutation database; genome sequencing paper curation

CPRG
http://www.broadinstitute.org/software/cprg
Interface for cancer program resources

GDAC
http://gdac.broadinstitute.org/
Data analysis; automatic pipelines; user-friendly reports

SNP500Cancer
http://snp500cancer.nci.nih.gov
Sequence and genotype verification of SNPs

canEvolve
www.canevolve.org/
Comprehensive analysis of tumor profile; Data from 90 studies involving more than 10,000 patients

MethyCancer
http://methycancer.psych.ac.cn
Relationship among DNA methylation, gene expression and cancer

SomamiR
http://compbio.uthsc.edu/SomamiR/
Correlation between somatic mutation and microRNA; genome-wide displaying

cBioPortal
http://www.cbioportal.org/public-portal/
Graphical summaries; gene alteration; processed data; visualization

UCSC Cancer Genomics Browser
https://genome-cancer.soe.ucsc.edu/
Clinical information; gene expression; copy number variation; visualization

CGWB
https://cgwb.nci.nih.gov/
Visualization; gene mutation and variation; automated analysis pipeline

GDSC
http://www.cancerrxgene.org
Drug sensitivity information; drug response information

canSAR
https://cansar.icr.ac.uk/
Multidisciplinary information; drug discovery

NONCODE
http://www.noncode.org/ ncRNAs;
lncRNAs; up-to-date and comprehensive resource

Address of the bookmark: http://omega.omicsbio.org/

So far miniasm is in early development stage. It has only been tested on a dozen of PacBio and Oxford Nanopore (ONT) bacterial data sets. Including the mapping step, it takes about 3 minutes to assemble a bacterial genome. Under the default setting, miniasm assembles 9 out of 12 PacBio datasets and 3 out of 4 ONT datasets into a single contig. The 12 PacBio data sets are PacBio E. coli sample, ERS473430, ERS544009, ERS554120, ERS605484, ERS617393, ERS646601, ERS659581, ERS670327, ERS685285, ERS743109 and a deprecated PacBio E. coli data set. ONT data are acquired from the Loman Lab.

For a C. elegans PacBio data set (only 40X are used, not the whole dataset), miniasm finishes the assembly, including reads overlapping, in ~10 minutes with 16 CPUs. The total assembly size is 105Mb; the N50 is 1.94Mb. In comparison, the HGAP3produces a 104Mb assembly with N50 1.61Mb. This dotter plot gives a global view of the miniasm assembly (on the X axis) and the HGAP3 assembly (on Y). They are broadly comparable. Of course, the HGAP3 consensus sequences are much more accurate. In addition, on the whole data set (assembled in ~30 min), the miniasm N50 is reduced to 1.79Mb. Miniasm still needs improvements.

Miniasm confirms that at least for high-coverage bacterial genomes, it is possible to generate long contigs from raw PacBio or ONT reads without error correction. It also shows that minimap can be used as a read overlapper, even though it is probably not as sensitive as the more sophisticated overlapers such as MHAP and DALIGNER. Coupled with long-read error correctors and consensus tools, miniasm may also be useful to produce high-quality assemblies.

Minimap and miniasm are ultrafast tools for (i) mapping and (ii) assembly. Designed for long, noisy reads, they do not have a correction or consensus step, and therefore the resulting assemblies are contiguous (i.e. long) but very noisy (i.e. full of errors)

We start with an all against all comparison:

minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq | gzip -1 > reads.paf.gz

Then we can assemble

miniasm -f reads.fq reads.paf.gz > reads.gfa

Convert GFA to FASTA:

awk '/^S/{print ">"$2"\n"$3}' reads.gfa | fold > reads.fa

And then count how many contigs:

grep ">" reads.fa | wc -l

# Download sample PacBio from the PBcR website
wget -O- http://www.cbcb.umd.edu/software/PBcR/data/selfSampleData.tar.gz | tar zxf -
ln -s selfSampleData/pacbio_filtered.fastq reads.fq
# Install minimap and miniasm (requiring gcc and zlib)
git clone https://github.com/lh3/minimap && (cd minimap && make)
git clone https://github.com/lh3/miniasm && (cd miniasm && make)
# Overlap
minimap/minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq | gzip -1 > reads.paf.gz
# Layout
miniasm/miniasm -f reads.fq reads.paf.gz > reads.gfa

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

Address of the bookmark: https://github.com/marbl/MashMap

https://github.com/ggonnella/rgfa

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5103826/

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