Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals.

Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ~10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package.

Availability: http://maq.sourceforge.net 

Contact: [email protected]

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Fast and accurate short read alignment with Burrows–Wheeler transform

Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source http://bowtie.cbcb.umd.edu.

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Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.

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An integrated encyclopedia of DNA elements in the human genome

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.

/pdf/model-based-analysis-of-chip-seq-macs-11m6g885d3.pdf

Model-based Analysis of ChIP-Seq (MACS)

Although de novo motifs can be discovered through mining over-represented sequence patterns, this approach misses some real motifs and generates many false positives. To improve accuracy, one solution is to consider some additional binding features (i.e. position preference and sequence rank preference). This information is usually required from the user. This paper presents a de novo motif discovery algorithm called SEME which uses pure probabilistic mixture model to model the motif's binding features and uses expectation maximization (EM) algorithms to simultaneously learn the sequence motif, position and sequence rank preferences without asking for any prior knowledge from the user. SEME is both efficient and accurate thanks to two important techniques: the variable motif length extension and importance sampling. Using 75 large scale synthetic datasets, 32 metazoan compendium benchmark datasets and 164 ChIP-Seq libraries, we demonstrated the superior performance of SEME over existing programs in finding transcription factor (TF) binding sites. SEME is further applied to a more difficult problem of finding the co-regulated TF (co-TF) motifs in 15 ChIP-Seq libraries. It identified significantly more correct co-TF motifs and, at the same time, predicted co-TF motifs with better matching to the known motifs. Finally, we show that the learned position and sequence rank preferences of each co-TF reveals potential interaction mechanisms between the primary TF and the co-TF within these sites. Some of these findings were further validated by the ChIP-Seq experiments of the co-TFs.

Simultaneously learning DNA motif along with its position and sequence rank preferences through EM algorithm

Locating the coding regions in a genome is one of the critical steps in understanding the genes in the genome. Aligning mRNAs with the whole genome provides clues about such locations. In eukaryotic genes, this alignment problem is complicated by the exon/intron structures. BLAT [6], one of the most popular mRNA alignment tools, was developed to address this issue. In general, for error-free mRNAs, BLAT is very efficient and accurate. However, the performance of BLAT degrades on two types of input: erroneous mRNAs and mRNAs without enough distinguishable short markers. Errors (about 10% in practice) in the mRNA as well as the genome sequence are rather common. Our experiments reveal that BLAT fails to locate mRNAs with 10% errors even if it runs in the “exhaustive” mode (i.e., a more accurate but much slower mode). For error-free mRNAs, we found that the efficiency of BLAT cannot be maintained in some rare cases which are characterized by mRNAs containing not enough distinguishable short markers; in such cases, BLAT has to run in the exhaustive mode and requires about 8 minutes per mRNA. In this paper, we give an efficient algorithm to align the above two types of mRNAs accurately. Our algorithm makes use of approximate string matching technique based on suffix tree and takes advantage of an effective structural filtering procedure. In our experiments, our algorithm consistently outperforms BLAT on these hard cases. In particular, our algorithm is able to locate all erroneous mRNAs (with the exact exon/intron structures); for those error-free mRNAs without enough distinguishable short markers, our algorithm takes 28 seconds per mRNA on average (i.e., 16 times faster than BLAT). Key-words: mRNA alignment, gene structures, exon/intron boundaries, erroneous mRNAs

Improving the Efficiency and Accuracy of aligning Erroneous mRNAS

Correction: Brief Overview of Bioinformatics Activities in Singapore

A transcript mapping method according to an embodiment of the invention is described hereinafter and combines short tag based (SAGE and MPSS) efficiency with the accuracy of full-length cDNA (flcDNA) for comprehensive characterization of transcriptomes. This method is also referred to as Gene Identification Signature (GIS) analysis. In this method, the 5′ and 3′ ends of full-length cDNA clones are initially extracted into a ditag structure, with the ditag concatemers of the ditag being subsequently sequenced in an efficient manner, and finally mapped to the genome for defining the gene structure. As a GIS ditag represents the 5′ and 3′ ends of a transcript, it is more informative than SAGE and MPSS tags. Segment lengths between 5′ and 3′ tag pairs are obtainable including orientation, ordering and chromosome family for efficient transcript mapping and gene location identification. Furthermore, a compressed suffix array (CSA) is used for indexing the genome sequence for improve mapping speed and to reduce computational memory requirements.

Transcript mapping method

This paper is concerned with maximum weight matchings of bipartite graphs. We show how to speed up the existing matching algorithms when the input graphs are node unbalanced or weight unbalanced. Based on these improved matching algorithms, we can solve efficiently a new matching problem called the hierarchical bipartite matching problem, and thus obtain a simple and faster algoirthm for finding the maximum agreement subtree of two labeled trees. The significance of our subtree algorithm lies in the fact that it matches or outperforms all previously known subtree algorithms that were designed for two special cases of labeled trees, namely, uniformly labeled trees and evolutionary trees.

Wing-Kin Sung

Papers

Simultaneously learning DNA motif along with its position and sequence rank preferences through EM algorithm

Improving the Efficiency and Accuracy of aligning Erroneous mRNAS

Correction: Brief Overview of Bioinformatics Activities in Singapore

Transcript mapping method

Unbalanced and Hierarchical Bipartite Matchings with Applications to Labeled Tree Comparison