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]

/pdf/fast-and-accurate-short-read-alignment-with-burrows-wheeler-43uqhgr33c.pdf

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.

/pdf/ultrafast-and-memory-efficient-alignment-of-short-dna-3mrm42jmnc.pdf

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.

/pdf/an-integrated-encyclopedia-of-dna-elements-in-the-human-52ukbi8ats.pdf

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)

A consensus tree is a phylogenetic tree that captures the similarity between a set of conflicting phylogenetic trees. The problem of computing a consensus tree is a major step in phylogenetic tree reconstruction. It also finds applications in predicting a species tree from a set of gene trees. This paper focuses on two of the most well-known and widely used oconsensus tree methods: the greedy consensus tree and the frequency difference consensus tree. Given $k$ conflicting trees each with $n$ leaves, the previous fastest algorithms for these problems were $O(k n^2)$ for the greedy consensus tree [J. ACM 2016] and $\tilde O(\min \{ k n^2, k^2n\})$ for the frequency difference consensus tree [ACM TCBB 2016]. We improve these running times to $\tilde O(k n^{1.5})$ and $\tilde O(k n)$ respectively.

A Faster Construction of Greedy Consensus Trees

One important way that gene expression data are often analysed in an unsupervised way is to cluster the samples without reference to any annotations about them. Before clustering, the data are often subjected to a feature selection preprocessing step, in which a subset of genes are chosen for further analysis. We examine the use of multimodality as a criterion for choosing genes in feature selection, and also propose a novel measure of pairwise dissimilarity to cluster the genes that have survived the preprocessing step. The resulting multiple gene subsets usually contain those that are more strongly correlated with the sample annotations of interest than those obtained through variance-based feature selection. Class discovery may be facilitated when gene expression data are analysed using the proposed method.

Multimodality as a criterion for feature selection in unsupervised analysis of gene expression data

The [Formula: see text] Consistency problem takes as input two sets [Formula: see text] and [Formula: see text] of resolved triplets and two sets [Formula: see text] and [Formula: see text] of fan triplets, and asks for a distinctly leaf-labeled tree that contains all elements in [Formula: see text] and no elements in [Formula: see text] as embedded subtrees, if such a tree exists. This article presents a detailed characterization of how the computational complexity of the problem changes under various restrictions. Our main result is an efficient algorithm for dense inputs satisfying [Formula: see text] whose running time is linear in the size of the input and therefore optimal.

Determining the Consistency of Resolved Triplets and Fan Triplets.

Molecular defects of Chinese patients with 21 hydroxylase deficiency

Cell-cycle associated promoter motif prediction is very important to understand the cell-cycle control and process. Modeling genome-wide gene expression as a function of the promoter sequence motif features has drawn great attention recently. The proposed techniques using this approach are not specific to cell-cycle associated motif discovery, hence find aperiodic motif weights across the time-course and lower sensitivity. Motifs are scored based on the successive model error reduction steps which may not reveal all relevant motifs since they are alternatives for the model. Another, drawback is, these methods output a list of sequences which may either contain several instances of a dominating motif box (a set of alternative sequence motifs) such as MCB or only a few instances of an important box. To address the above problems, we propose a multi-step constrained optimization based position weight matrix (PWM) motif finding methodology called ConstrainedMotif. It models the cell-cycle regulated gene expression as a linear function of the motif features while the weights of them are constrained to be periodic across the time-course. The score of a motif is the error reduction in the prediction by that motif alone. The multi-step modeling starts with a set of sequences and output a ranked list of cell-cycle associated PWM motifs. We evaluate this methodology using S. Cerevesiae cell-cycle data published by Spellman et al. The results show that ConstrainedMotif is more sensitive and most of the instances of the boxes are represented by the respective matching PWMs.

/pdf/constrainedmotif-a-periodicity-constraint-based-algorithm-to-159e6q1b1t.pdf

Wing-Kin Sung

Papers

A Faster Construction of Greedy Consensus Trees

Multimodality as a criterion for feature selection in unsupervised analysis of gene expression data

Determining the Consistency of Resolved Triplets and Fan Triplets.

Molecular defects of Chinese patients with 21 hydroxylase deficiency

ConstrainedMotif: a periodicity constraint based algorithm to predict cell-cycle associated promoter motifs using time-course gene expression data