Revision as of 15:40, 15 July 2011

shore preprocess

shore preprocess creates the mapping indices, calculates local GC content and sequence complexity. In addition, SHORE will create a new copy of the fasta file of the reference sequence featuring adjusted chromosome/contig ids and write all files to the IndexFolder.

shore import

This program converts Illumina GAPipeline BUSTARD directories, FASTQ files or SOLiD csfasta files into SHORE format. shore import will create the necessary files and directory structure.

Input formats of the importer are specified using option -v. Available importers are:

• Bustard: Input generated by the GAPipeline (bustard/goat) or SCS programs.
• Fastq: FastQ files. Some users prefer Illumina fastq files as standard output from the GAPipeline.
• Solid: SOLiD F3 and R3 csfasta and (optionally) QV files.
• Shore: SHORE reads_0.fl files. This importer can be used to re-filter or trim reads which are already in SHORE format. In addition, 454 SFF files will also be accepted by this importer.

shore mapflowcell

This program performs the actual read alignments to a reference genome.

SHORE supports various mapping tools to always provide the best option for various applications. The default tool, GenomeMapper, is extensively tested. Currently the other available options are BWA, Bowtie, Novocraft and Eland.

shore correct4pe

shore correct4pe finds the most likely mapping of repetitive reads by utilizing paired-end information. While in paired read mapping each read is aligned separately, read pair information can be used to increase the likelihood of an alignment by selecting the paired alignment based on the most likely distance between the pairs.

shore correct4pe starts by estimating the insert size distribution. The upper bound of this distribution is usually very sharp (clones longer than expected seem to be very rare), whereas the lower boundary is more blurred and very small clones can be observed as well. The insert size distribution is then translated into a probability distribution for the observation of a given distance of a pairing (where pairing is defined as the combination of one of the mappings of read 1 with one of the mappings of read 2). All possible combinations of the mappings of both reads of a pair are compared and all pairings with a probability equal to zero are dismissed. Mappings which are not in a pairing with a probability above zero are deleted. This removes all repetitive mappings, which resulted from repeats. If there is a mapping of one read pair with two different mappings of the other read the more likely pairing is kept. If all pairings have zero probability all mappings of both reads are kept. These are the discordant (unhappy) read pairs which typically are used to predict structural variants.

shore correct4pe will plot the insert size distribution using the R if -p is specified. In this case R has to be installed and included in the PATH environment variable.

shore merge

Merges and filters alignment files

shore mapview

Text-based alignment visualization

shore consensus

shore consensus has been replaced by shore qvar.

The common output from whole genome re-sequencing projects are lists of all identified polymorphisms (e.g. SNPs, indels, CNVs) as well as reference-like positions. In addition a consensus sequence or contigs can be generated by combining all high quality predictions. shore consensus provides this functionality by sequentially scanning an alignment to gather all read information available at a specific locus (i.e. called bases, base qualities, coverage, repetitiveness, alignment quality). This information is subsequently used to predict differences to the reference sequence.

shore consensus can also be used to identify minor alleles (SNPs or short indels) in pooled samples. In addition shore consensus estimates several characteristics of a run ahead of the actual consensus calling. This includes min and max read length, min and max mismatches, sequencing depth, observed local repetitiveness and GC content bias. Consensus also provides multiple project statistics regarding sequencing error rate, correlation of quality values to observed errors and coverage biases due to local GC content, which can be used to optimize further analysis (e.g. deletions should not be called in low GC content regions if a strong GC bias is observed).

Note: shore consensus can also be applied to sRNA-seq, mRNA-seq and ChIP-seq data. However, SHORE provides more appropriate tools for those purposes (coverage and peak).

shore qVar

Computes consensus sequence, SNPs, indels and CNVs from alignments

shore methyl

Quantify methylated and unmethylated cytosines from BS-seq alignments (only genomemapper)

shore coverage

For analysis of expression levels of mRNAs and small RNAs or for detection of unknown transcripts it is typically required to generate a coverage graph and to define expressed segments based on consecutive coverage.

shore coverage generates a coverage graph by sequentially scanning the alignment and basically counting reads.

shore peak

shore peak provides enriched region prediction for ChIP-Seq experiments. Significance of the predicted regions is assessed by comparison to the specified control samples.

Replicate experiments may be processed simultaneously by specifying multiple experiment and control paths. While the significance of each peak region is then tested for independently for each replicate, the region prediction itself is performed jointly for all experiments to obtain results that are immediately comparable.

shore srna

The purpose of shore srna is facilitating the analysis of small RNA sequencing data. The genome is scanned for regions where significant amounts of small RNAs are expressed and annotates these loci by read counts as well as the sRNA size that predominates.

shore tagstats

Gather read statistics for multiple samples without a reference sequence

shore structure

shore structure enables the detection of diverged regions through clustering of mate pairs alignments with an unexpected distance and/or orientation to each other. Typically the recall is very good for deletions, but insertions longer than the insert size are cannot be revealed. In addition shore structure calls inversions. Currently only works for homozygous changes.

shore count

shore count calculates the read count as well as other properties for regions in the genome that have already been defined by some other means. It may be used to analyze either fixed-size jumping windows over the genome or regions defined in an input file, e.g. to analyze annotated coding regions or to manually re-analyze regions defined by the segmentation algorithms of shore coverage, shore peak or shore srna.

Accepted input files are tab-delimited plain text files with a header specifying the columns chr, pos, size and optionally strand.

shore binom_test

shore binom_test can be used to evaluate two sets of count data agaist each other using a binomial test.

shore mtc

The subprogram shore mtc implements various multiple testing correction methods. The expected input is a tab-delimited text file with a header, and the column containing the p-values to be adjusted must be named raw_p.

Implemented methods include

• Benjamini-Hochberg false discovery rate control (fdr_bh)
• Bonferroni familywise error rate control (fwer_bonferroni)
• Holm familywise error rate control (fwer_holm)
• Hochberg familywise error rate control (fwer_hochberg)
• Sidak singlestep familywise error rate control (fwer_sidak_ss)
• Sidak stepdown familywise error rate control (fwer_sidak_sd)
• Benjamini-Yekutieli false discovery rate control (fdr_by).

shore annotate_region

Relate loci to annotation

shore convert

Convert SHORE files into common file formats, and vice versa

shore sort

Sort / merge tab-delimited text files

shore compress

Compress files to indexed gzip format

shore 2dex

Range-indexing and query for tab-delimited text files

shore idtrans

Translate SHORE sequence IDs into sequence names, and vice versa