Eland alignment tool

All works. Hello I found your blog whilst searching for details about the way that Eland actually works. I understand your description of the algorithm but what interests me is the data structure that is used to hold the reads in RAM and which the algorithm searches over or in SOAPs case the reference.

Do you know how this is done? Though the algorithm is relatively involved, using look up tables and such, they appear to be using a suffix tree to hold the oligos. I hope that helps. Hi Anthony, I just discovered your blog and it looks very interesting to me! Aligned hits should contain the enzyme site, and have at most one mismatch in the tag region. Evaluated on a real dataset containing 9 Illumina-Solexa single-end resequencing reads length 32 bp , which were generated from a 5 Mb human genome region, SOAP was almost gapped to ungapped times faster than blastn, while having better sensitivity Table 1.

The iterative feature of SOAP improved sensitivity. And gapped alignment can further identify hits accommodating small indels which compose only a small fraction of all hits but are a very important class of mutation.

Since SOAP loads reference sequences into memory, while Eland and Maq load reads, the memory usage varies in different datasets. Comparison of performance and sensitivity among short oligonucleotide alignment programs.

We used a query dataset containing 9 single-end reads length 32 bp generated by Illumina-Solexa Genome Analyzer. For blat, tileSize parameter was set at 8. Sensitivity is calculated under the same threshold by allowing at most 2 mismatches. SOAP gapped will allow one continuous insertion or deletion with size between 1 to 3 bp. After checking sequencing quality, we found the remaining unmappable reads are in low sequencing quality.

It's a command-driven program, which employs single command line model and batch computing model. On batch computing model, the reference sequences and hash index tables will reside in the memory and alignment procedure can be performed for multiple query datasets in a order.

It supports multithreaded parallel computing. We thank Shengting Li for setting up the website. Google Scholar. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide.

Sign In or Create an Account. Sign In. Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume This has the effect of significantly reducing the search space. Jump to: navigation , search. Personal tools Log in. Services Sequencing.

New high-throughput DNA sequence technologies play an important role in modern life science research. These high-throughput methods, such as the Illumina and Life Sciences technologies, produce a large volume of sequence data, which can be used for a variety of tasks including genome resequencing and genome-wide polymorphism discovery.

These methods produce a large set thousands or millions of short sequences that often must be mapped to a genome, allowing for only a few errors.

To address this computational problem, several programs have been developed. Cox, ELAND: efficient local alignment of nucleotide data, unpublished data , which is a part of the data analysis pipeline for the Illumina analyzer, is designed to search DNA databases for a large number of short sequences. To speedup the data processing, ELAND performs only ungapped alignments allowing up to two mismatches.

MAQ H. In addition, using sequence quality information, MAQ measures the error probability of alignments. SOAP Li et al. For example, SOAP will find an alignment with one continuous gap of size 2, but will not find an alignment with one gap and one mismatch.

SeqMap Jiang et al. However, its performance is much slower than SOAP and degrades significantly once the gaps are allowed.

Unfortunately, as predicted by Amdahl's law Amdahl, , the overall gain using these faster methods is limited and does not adequately address the pressing end-to-end computational problem of rapidly and accurately mapping a large number of oligonucleotide sequences against entire genomes. In this work, we address this challenge and present an efficient and highly sensitive sequence alignment technique for a large set of short sequences.

Unlike most of the existing methods, our program allows for a richer match model and finds gapped and ungapped alignments with up to three errors of any error combinations mismatch, insertion and deletion. The ability to identify both gapped and ungapped alignments has the added advantage of being able to detect multiple classes of mutations: single nucleotide variations SNVs and insertions or deletions indels.

The genetic variation provided by each class of mutations can occur in coding or regulatory regions thereby altering the function of important proteins which have a major impact on human health and susceptibility to disease. It is interesting to note that a large number of reads are not mapped. A likely reason, based on our experience and other labs that are working with the relatively new Illumina technology, is that these may be due to adaptor sequences, low-quality sequences, sequences with many error in base calling, contaminants, etc.

We plan on exploring these issues further in the future as we gain more experience with this technology.