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 widely-used method for determining the similarity of two labeled trees is to compute a maximum agreement subtree of the two trees Previous work on this similarity measure only concerns with the comparison of labeled trees of two special kinds, namely, uniformly labeled trees (ie, trees with all their nodes labeled by the same symbol) and evolutionary trees (ie, leaf-labeled trees with distinct symbols for distinct leaves) This paper presents an algorithm for comparing trees that are labeled in an arbitrary manner In addition to the generalization, our algorithm is faster than the previous algorithms in many cases

A Faster and Unifying Algorithm for Comparing Trees

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 is also central for predicting a species tree from a set of gene trees, as indicated recently in [Nature 2013].
This paper focuses on two of the most well-known and widely used consensus 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 O~(min{k n^2, k^2n}) for the frequency difference consensus tree [ACM TCBB 2016]. We improve these running times to O~(k n^{1.5}) and O~(k n) respectively.

https://drops.dagstuhl.de/opus/volltexte/2018/9067/pdf/LIPIcs-ICALP-2018-63.pdf/

A Faster Construction of Greedy Consensus Trees

Drosophila melanogaster is one of the most important organisms for studying the genetics of development. The precise regulation of genes during early development is enacted through the control of transcription. The control circuitry is hardwired in the genome as clusters of multiple transcription factor binding sites (TFBS) known as cis-regulatory modules (CRMs). A number of TFBS and CRMs have been experimentally annotated in the Drosophila genome. Currently about 661 CRM sequences are known, of which 155 have been annotated with 778 TFBS. This work attempts computational annotation of TFBS in the remaining 506 uncharacterized Drosophila CRMs. The difficulty of this task lies in the fact that experimental data is insufficient for constructing reliable positional weight matrices (PWM) to predict the TFBS. Thus a novel feature extraction and classification method for TFBS detection has been implemented in this work. The method achieves both high sensitivity and low false positive rate in cross-validation studies. As a result of this work, a new database has been compiled which aggregates all the CRM and TFBS annotation information for Drosophila available to date, and appends new TFBS annotations.

/pdf/computational-annotation-of-transcription-factor-binding-4hjl1uh1b8.pdf

Computational Annotation of Transcription Factor Binding Sites in D. Melanogaster Developmental Genes

The evolutionary history of certain species such as polyploids are modeled by a generalization of phylogenetic trees called multi-labeled phylogenetic trees, or MUL trees for short. One problem that relates to inferring a MUL tree is how to construct the smallest possible MUL tree that is consistent with a given set of rooted triplets, or SMRT problem for short. This problem is NP-hard. There is one algorithm for the SMRT problem which is exact and runs in time, where is the number of taxa. In this paper, we show that the SMRT does not seem to be an appropriate solution from the biological point of view. Indeed, we present a heuristic algorithm named MTRT for this problem and execute it on some real and simulated datasets. The results of MTRT show that triplets alone cannot provide enough information to infer the true MUL tree. So, it is inappropriate to infer a MUL tree using triplet information alone and considering the minimum number of duplications. Finally, we introduce some new problems which are more suitable from the biological point of view.

/pdf/do-triplets-have-enough-information-to-construct-the-multi-3z7rdp5cq2.pdf

Do Triplets Have Enough Information to Construct the Multi-Labeled Phylogenetic Tree?

The present invention provides a method for quantifying the level of at least two types of selected proteins by immuno-amplification RNA comprising: (a) Immobilizing at least 2 species of capture molecules onto an insoluble support, wherein each species of capture molecule is specific for a species of a target protein; (b) Contacting the support with the at least 2 species of target proteins so that the target proteins bind to the immobilised capture molecules; (c) Contacting the support wherein the target proteins are immobilised with at least two species of RNA promoter driven DNA sequences each being different from the other and being conjugated to a detection molecule specific to each species of the target proteins so that each detection molecule binds to the specific protein on the support, wherein the promoter-driven DNA sequence comprises a template comprising a 1st region which is a RNA polymerase promoter and a 2nd region comprising a probe, the probe having a nucleic acid sequence specific for each target protein; (d) Obtaining amplified copies of at least two species of RNAs each representing one species of target protein; (e) Quantifying the level of the target species of proteins by detecting the species of RNAs. This method is of particular use in the determinations where the target proteins are cytokines and the detection and capture molecules are antibodies.

Wing-Kin Sung

Papers

A Faster and Unifying Algorithm for Comparing Trees

A Faster Construction of Greedy Consensus Trees

Computational Annotation of Transcription Factor Binding Sites in D. Melanogaster Developmental Genes

Do Triplets Have Enough Information to Construct the Multi-Labeled Phylogenetic Tree?

Immuno-amplification rna assay