A backward procedure for change-point detection with applications to copy number variation detection

Change-point detection regains much attention recently for analyzing array or sequencing data for copy number variation (CNV) detection. In such applications, the true signals are typically very short and buried in the long data sequence, which makes it challenging to identify the variations efficiently and accurately. In this article, we propose a new change-point detection method, a backward procedure, which is not only fast and simple enough to exploit high-dimensional data but also performs very well for detecting short signals. Although motivated by CNV detection, the backward procedure is generally applicable to assorted change-point problems that arise in a variety of scientific applications. It is illustrated by both simulated and real CNV data that the backward detection has clear advantages over other competing methods especially when the true signal is short

VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research

Accurate variant calling in next generation sequencing (NGS) is critical to understand cancer genomes better. Here we present VarDict, a novel and versatile variant caller for both DNA- and RNA-sequencing data. VarDict simultaneously calls SNV, MNV, InDels, complex and structural variants, expanding the detected genetic driver landscape of tumors. It performs local realignments on the fly for more accurate allele frequency estimation. VarDict performance scales linearly to sequencing depth, enabling ultra-deep sequencing used to explore tumor evolution or detect tumor DNA circulating in blood. In addition, VarDict performs amplicon aware variant calling for polymerase chain reaction (PCR)-based targeted sequencing often used in diagnostic settings, and is able to detect PCR artifacts. Finally, VarDict also detects differences in somatic and loss of heterozygosity variants between paired samples. VarDict reprocessing of The Cancer Genome Atlas (TCGA) Lung Adenocarcinoma dataset called known driver mutations in KRAS, EGFR, BRAF, PIK3CA and MET in 16% more patients than previously published variant calls. We believe VarDict will greatly facilitate application of NGS in clinical cancer research

Automated linear motif discovery from protein interaction network

Master'sMASTER OF SCIENC

A fast and effective approach for the detection of units in tandem repeat proteins

openA fast and effective approach for the detection of units in tandem repeat proteinsA fast and effective approach for the detection of units in tandem repeat protein

Computational analysis of human genomic variants and lncRNAs from sequence data

The high-throughput sequencing technologies have been developed and applied to the human genome studies for nearly 20 years. These technologies have provided numerous research applications and have significantly expanded our knowledge about the human genome. In this thesis, computational methods that utilize sequence data to study human genomic variants and transcripts were evaluated and developed. Indel represents insertion and deletion, which are two types of common genomic variants that are widespread in the human genome. Detecting indels from human genomes is the crucial step for diagnosing indel related genomic disorders and may potentially identify novel indel makers for studying certain diseases. Compared with previous techniques, the high-throughput sequencing technologies, especially the next- generation sequencing (NGS) technology, enable to detect indels accurately and efficiently in wide ranges of genome. In the first part of the thesis, tools with indel calling abilities are evaluated with an assortment of indels and different NGS settings. The results show that the selection of tools and NGS settings impact on indel detection significantly, which provide suggestions for tool selection and future developments. In bioinformatics analysis, an indel’s position can be marked inconsistently on the reference genome, which may result in an indel having different but equivalent representations and cause troubles for downstream. This problem is related to the complex sequence context of the indels, for example, short tandem repeats (STRs), where the same short stretch of nucleotides is amplified. In the second part of the thesis, a novel computational tool VarSCAT was described, which has various functions for annotating the sequence context of variants, including ambiguous positions, STRs, and other sequence context features. Analysis of several high- confidence human variant sets with VarSCAT reveals that a large number of genomic variants, especially indels, have sequence features associated with STRs. In the human genome, not all genes and their transcripts are translated into proteins. Long non-coding ribonucleic acid (lncRNA) is a typical example. Sequence recognition built with machine learning models have improved significantly in recent years. In the last part of the thesis, several machine learning-based lncRNA prediction tools were evaluated on their predictions for coding potentiality of transcripts. The results suggest that tools based on deep learning identify lncRNAs best. Ihmisen genomivarianttien ja lncRNA:iden laskennallinen analyysi sekvenssiaineistosta Korkean suorituskyvyn sekvensointiteknologioita on kehitetty ja sovellettu ihmisen genomitutkimuksiin lähes 20 vuoden ajan. Nämä teknologiat ovat mahdollistaneet ihmisen genomin laaja-alaisen tutkimisen ja lisänneet merkittävästi tietoamme siitä. Tässä väitöstyössä arvioitiin ja kehitettiin sekvenssiaineistoa hyödyntäviä laskennallisia menetelmiä ihmisen genomivarianttien sekä transkriptien tutkimiseen. Indeli on yhteisnimitys lisäys- eli insertio-varianteille ja häviämä- eli deleetio-varianteille, joita esiintyy koko genomin alueella. Indelien tunnistaminen on ratkaisevaa geneettisten poikkeavuuksien diagnosoinnissa ja eri sairauksiin liittyvien uusien indeli-markkereiden löytämisessä. Aiempiin teknologioihin verrattuna korkean suorituskyvyn sekvensointiteknologiat, erityisesti seuraavan sukupolven sekvensointi (NGS) mahdollistavat indelien havaitsemisen tarkemmin ja tehokkaammin laajemmilta genomialueilta. Väitöstyön ensimmäisessä osassa indelien kutsumiseen tarkoitettuja laskentatyökaluja arvioitiin käyttäen laajaa valikoimaa indeleitä ja erilaisia NGS-asetuksia. Tulokset osoittivat, että työkalujen valinta ja NGS-asetukset vaikuttivat indelien tunnistukseen merkittävästi ja siten ne voivat ohjata työkalujen valinnassa ja kehitystyössä. Bioinformatiivisessa analyysissä saman indelin sijainti voidaan merkitä eri kohtiin referenssigenomia, joka voi aiheuttaa ongelmia loppupään analyysiin, kuten indeli-kutsujen arviointiin. Tämä ongelma liittyy sekvenssikontekstiin, koska variantit voivat sijoittua lyhyille perättäisille tandem-toistojaksoille (STR), jossa sama lyhyt nukleotidijakso on monistunut. Väitöstyön toisessa osassa kehitettiin laskentatyökalu VarSCAT, jossa on eri toimintoja, mm. monitulkintaisten sijaintitietojen, vierekkäisten alueiden ja STR-alueiden tarkasteluun. Luotettaviksi arvioitujen ihmisen varianttiaineistojen analyysi VarSCAT-työkalulla paljasti, että monien geneettisten varianttien ja erityisesti indelien ominaisuudet liittyvät STR-alueisiin. Kaikkia ihmisen geenejä ja niiden geenituotteita, kuten esimerkiksi ei-koodaavia RNA:ta (lncRNA) ei käännetä proteiiniksi. Koneoppimismenetelmissä ja sekvenssitunnistuksessa on tapahtunut huomattavaa parannusta viime vuosina. Väitöstyön viimeisessä osassa arvioitiin useiden koneoppimiseen perustuvien lncRNA-ennustustyökalujen ennusteita. Tulokset viittaavat siihen, että syväoppimiseen perustuvat työkalut tunnistavat lncRNA:t parhaiten

Development of Computational Techniques for Regulatory DNA Motif Identification Based on Big Biological Data

Accurate regulatory DNA motif (or motif) identification plays a fundamental role in the elucidation of transcriptional regulatory mechanisms in a cell and can strongly support the regulatory network construction for both prokaryotic and eukaryotic organisms. Next-generation sequencing techniques generate a huge amount of biological data for motif identification. Specifically, Chromatin Immunoprecipitation followed by high throughput DNA sequencing (ChIP-seq) enables researchers to identify motifs on a genome scale. Recently, technological improvements have allowed for DNA structural information to be obtained in a high-throughput manner, which can provide four DNA shape features. The DNA shape has been found as a complementary factor to genomic sequences in terms of transcription factor (TF)-DNA binding specificity prediction based on traditional machine learning models. Recent studies have demonstrated that deep learning (DL), especially the convolutional neural network (CNN), enables identification of motifs from DNA sequence directly. Although numerous algorithms and tools have been proposed and developed in this field, (1) the lack of intuitive and integrative web servers impedes the progress of making effective use of emerging algorithms and tools; (2) DNA shape has not been integrated with DL; and (3) existing DL models still suffer high false positive and false negative issues in motif identification. This thesis focuses on developing an integrated web server for motif identification based on DNA sequences either from users or built-in databases. This web server allows further motif-related analysis and Cytoscape-like network interpretation and visualization. We then proposed a DL framework for both sequence and shape motif identification from ChIP-seq data using a binomial distribution strategy. This framework can accept as input the different combinations of DNA sequence and DNA shape. Finally, we developed a gated convolutional neural network (GCNN) for capturing motif dependencies among long DNA sequences. Results show that our developed web server enables providing comprehensive motif analysis functionalities compared with existing web servers. The DL framework can identify motifs using an optimized threshold and disclose the strong predictive power of DNA shape in TF-DNA binding specificity. The identified sequence and shape motifs can contribute to TF-DNA binding mechanism interpretation. Additionally, GCNN can improve TF-DNA binding specificity prediction than CNN on most of the datasets