Wing-Kin Sung

Bio: Wing-Kin Sung is an academic researcher from National University of Singapore. The author has contributed to research in topics: Gene & Chromatin immunoprecipitation. The author has an hindex of 64, co-authored 327 publications receiving 26116 citations. Previous affiliations of Wing-Kin Sung include University of Hong Kong & Yale University.

Comprehensive long-span paired-end-tag mapping reveals characteristic patterns of structural variations in epithelial cancer genomes.

Abstract: Somatic genome rearrangements are thought to play important roles in cancer development. We optimized a long-span paired-end-tag (PET) sequencing approach using 10-Kb genomic DNA inserts to study human genome structural variations (SVs). The use of a 10-Kb insert size allows the identification of breakpoints within repetitive or homology-containing regions of a few kilobases in size and results in a higher physical coverage compared with small insert libraries with the same sequencing effort. We have applied this approach to comprehensively characterize the SVs of 15 cancer and two noncancer genomes and used a filtering approach to strongly enrich for somatic SVs in the cancer genomes. Our analyses revealed that most inversions, deletions, and insertions are germ-line SVs, whereas tandem duplications, unpaired inversions, interchromosomal translocations, and complex rearrangements are over-represented among somatic rearrangements in cancer genomes. We demonstrate that the quantitative and connective nature of DNA–PET data is precise in delineating the genealogy of complex rearrangement events, we observe signatures that are compatible with breakagefusion-bridge cycles, and we discover that large duplications are among the initial rearrangements that trigger genome instability for extensive amplification in epithelial cancers. [Supplemental material is available for this article. The sequencing data from this study have been submitted to NCBI Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo) under accession no. GSE26954.]

Transcriptional consequences of genomic structural aberrations in breast cancer.

Abstract: Using a long-span, paired-end deep sequencing strategy, we have comprehensively identified cancer genome rearrangements in eight breast cancer genomes. Herein, we show that 40%–54% of these structural genomic rearrangements result in different forms of fusion transcripts and that 44% are potentially translated. We find that single segmental tandem duplication spanning several genes is a major source of the fusion gene transcripts in both cell lines and primary tumors involving adjacent genes placed in the reverse-order position by the duplication event. Certain other structural mutations, however, tend to attenuate gene expression. From these candidate gene fusions, we have found a fusion transcript (RPS6KB1–VMP1) recurrently expressed in ∼30% of breast cancers associated with potential clinical consequences. This gene fusion is caused by tandem duplication on 17q23 and appears to be an indicator of local genomic instability altering the expression of oncogenic components such as MIR21 and RPS6KB1.

A Space and Time Efficient Algorithm for Constructing Compressed Suffix Arrays

Abstract: With the first human DNA being decoded into a sequence of about 28 billion characters, much biological research has been centered on analyzing this sequence Theoretically speaking, it is now feasible to accommodate an index for human DNA in the main memory so that any pattern can be located efficiently This is due to the recent breakthrough on compressed suffix arrays, which reduces the space requirement from O(n log n) bits to O(n) bits However, constructing compressed suffix arrays is still not an easy task because we still have to compute suffix arrays first and need a working memory of O(n log n) bits (ie, more than 13 gigabytes for human DNA) This paper initiates the study of constructing compressed suffix arrays directly from the text The main contribution is a construction algorithm that uses only O(n) bits of working memory, and the time complexity is O(n log n) Our construction algorithm is also time and space efficient for texts with large alphabets such as Chinese or Japanese Precisely, when the alphabet size is |Σ|, the working space is O(n log |Σ|) bits, and the time complexity remains O(n log n), which is independent of |Σ|

Fast and accurate short read alignment with Burrows–Wheeler transform

Abstract: 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]

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

Abstract: 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.

An integrated encyclopedia of DNA elements in the human genome

Abstract: 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.

Machine learning

Abstract: 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.).

Model-based Analysis of ChIP-Seq (MACS)

Abstract: 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.