LSC

  • LSC is a long read error correction tool.
    It offers fast correction with high sensitivity
    and good accuracy.
  •  
  •     

Latest News: Minor updates and bug fixes to LSC 1.beta ... read more

Manual

Installation

No explicit installation is required for LSC. You may copy the LSC binaries to any location as long as all the binaries (including Novoalign) are in the same directory or path.

But you need to Python2.6 installed in your computer. The modules "numpy" and "scipy" are also required. Please see LSC requirements for more details

Using LSC

Firstly, see the tutorial on how to use LSC on some example data.

In order to use LSC on your own data:

  1. Create an empty directory, this will be the working directory.
  2. Copy "run.cfg" from the LSC package to the working directory.
  3. Edit run.cfg to include paths to your python binary directory, data files and the paths of the temp folder and the output folder. You may also want to configure other parameters default settings. LSC uses external aligners to find short reads to long reads mappings. Latest LSC version supports BWA, Bowtie2, Novoalign and RazerS (v3) aligners. The aligners and their setting options could be modified through run.cfg file. The path to selected aligner should be set in your PATH env variable.
  4. Execute "/home/user/LSC_path/runLSC.py run.cfg" while in your working directory. or Execute "runLSC.py run.cfg, if all LSC executable files are in the default bin
  5. After a certain time execution will conclude. You can find results in the "output" directory.

Module: runLSC

"runLSC.py" is the main program in the LSC package. It calls other modules to run the full error correction on your data. Output is written to the "output" folder. Details of the output files are described in file formats. Its options are described in run.cfg file. You just need to run "runLSC.py" with a configuration file "run.cfg":

/home/user/LSC_path/runLSC.py run.cfg
 or If you have put all LSC executable files in the default path:
runLSC.py run.cfg
In vesion 3.0 or later, you can run LSC in two steps using 'mode' option in run.cfg. If for a reason LSC was terminated in correction step, you can restart the correction step without re-running the LR-SR mapping step.
Other LSC options:
 To check LSC version: runLSC.py -v 
 To clean up temp folderpath (if you used "cleap_up=0" option): runLSC.py run.cfg -clean_up 
Note: temp_path, Nthreads1 and Nthreads2 from run.cfg file are used for removing intermediate files.

Input files

LSC accepts one long-read sequences file (to be corrected) and one short-read sequences file as input. The input files could be in standard fasta or fastq formats. Note: As part of LSC algorithm, it generates homopolyer-compressed short-read sequences before alignment. If you have already run LSC with the same SR dataeset you can skip this step by using previously generated homopolyer-compressed SR files. (You can find SR.fa.cps and SR.fa.idx in temp folderpath.)
The file locations and their format should be set in run.cfg file through pathfilename and filetype options.

Output files

There are four output files: corrected_LR.fa, corrected_LR.fq, full_LR.fa, uncorrected_LR.fa in output folder:

The quality (error rate) of corrected reads in corrected_LR.fq depends on its SR coverage and it uses Sanger standard encoding.

Reference: LSC paper
* Error probablity is modeled with logarithmic funtion fitted to real data error-probabilities computed in the paper.
SRs CoverageError Probability*
00.275
10.086
20.063
30.051
40.041
50.034
60.028
70.023
80.018
90.014
100.011
110.008
120.005
130.002
>= 14~0.000

Note: Part of corrected_LR sequence without any short read coverage would have the default 27.5% error rate. If input LRs are in fastq format, the original quality values are not used here.

Module: filter_corrected_reads.py

In addition to quality information in corrected_LR.fq file, you can also select corrected LR sequences with higher percentage of SR covered length using filter_corrected_reads.py script in the bin folder.

LSC_bin_path/filter_corrected_reads.py <SR_covered_length_threshold> <corrected_LR.fa or fq file> > <output_file>

exapmle:     python bin/filter_corrected_reads.py 0.5 output/corrected_LR.fa > output/corrected_LR.filtered.fa

You can also select "best" reads for your downstream analysis by mapping corrected LRs to the reference genome or annotation (for RNA-seq analysis). Then, filter the reads by mapping score or percentage of base match (e.g. "identity" in BLAT)

Short read-Long read Aligner

LSC uses a short read aligner in the first step. By default, Bowtie2 is used. You can have BWA, , Novoalign or RazerS (v3) to run this step as well.

Default aligners setting are:

    BWA : -n 0.01 -o 10 -e 3 -d 0 -i 0 -M 1 -O 0 -E 1 -N
    Novoalign : -t 0,1.5 -g 0 -x 20 -r Ex 1000 -R 500 -o Sa
    RazerS3 : -i 92
You can change these settings through .cfg file. Please refer to their manuals for more details.

Following figures compare LSC correction results configured with different supported aligners. Identity metric is defined as number-of-matchs/error-corrected-read-length after aligning reads to reference genome using Blat.


Data-set:


Based on your system configuration, you can select the aligner which fits better with your CPU or Memory resources.

Short-read coverage depth (SCD)

LSC uses consensus of short-read mapping results to correct long read sequences. In case of having high SR coverage, pile of SRs mapped to a LR segment would significantly increase running time and memory usage in correction step, while having repetitive (redundant) information. By setting SCD parameter in run.cfg file, LSC uses a probabilistic algorithm to randomly select bounded number of SR alignemt results for each LR region in order to maintain expected SR coverage depth of SCD value. This would eliminate high memory peaks in corection step due to pile of SRs mapped in high coverage or repetitive regions. Based on our experiment on multilpe datasets, setting SCD = 20 gave comparable results w.r.t SCD = -1 (using all alignment results,i.e. without any bounded coverage limit).

Execution Time

Following CPU and execution times are suggested-usage using LSC.0.2.2 and LSC 1.alpha on our clusters with six thread. These figures will greatly differ based on your system configuration.

100,000 PacBio long reads X 64 million 75bp Illumina short reads (Dataset)