A Genomic Distance Based on MUM Indicates Discontinuity between Most Bacterial Species and Genera

The fundamental unit of biological diversity is the species. However, a remarkable extent of intraspecies diversity in bacteria was discovered by genome sequencing, and it reveals the need to develop clear criteria to group strains within a species. Two main types of analyses used to quantify intraspecies variation at the genome level are the average nucleotide identity (ANI), which detects the DNA conservation of the core genome, and the DNA content, which calculates the proportion of DNA shared by two genomes. Both estimates are based on BLAST alignments for the definition of DNA sequences common to the genome pair. Interestingly, however, results using these methods on intraspecies pairs are not well correlated. This prompted us to develop a genomic-distance index taking into account both criteria of diversity, which are based on DNA maximal unique matches (MUM) shared by two genomes. The values, called MUMi, for MUM index, correlate better with the ANI than with the DNA content. Moreover, the MUMi groups strains in a way that is congruent with routinely used multilocus sequence-typing trees, as well as with ANI-based trees. We used the MUMi to determine the relatedness of all available genome pairs at the species and genus levels. Our analysis reveals a certain consistency in the current notion of bacterial species, in that the bulk of intraspecies and intragenus values are clearly separable. It also confirms that some species are much more diverse than most. As the MUMi is fast to calculate, it offers the possibility of measuring genome distances on the whole database of available genomes

The LabelHash algorithm for substructure matching

Background: There is an increasing number of proteins with known structure but unknown function. Determining their function would have a significant impact on understanding diseases and designing new therapeutics. However, experimental protein function determination is expensive and very time-consuming. Computational methods can facilitate function determination by identifying proteins that have high structural and chemical similarity. Results: We present LabelHash, a novel algorithm for matching substructural motifs to large collections of protein structures. The algorithm consists of two phases. In the first phase the proteins are preprocessed in a fashion that allows for instant lookup of partial matches to any motif. In the second phase, partial matches for a given motif are expanded to complete matches. The general applicability of the algorithm is demonstrated with three different case studies. First, we show that we can accurately identify members of the enolase superfamily with a single motif. Next, we demonstrate how LabelHash can complement SOIPPA, an algorithm for motif identification and pairwise substructure alignment. Finally, a large collection of Catalytic Site Atlas motifs is used to benchmark the performance of the algorithm. LabelHash runs very efficiently in parallel; matching a motif against all proteins in the 95 % sequence identity filtered non-redundant Protein Data Bank typically takes no more than a few minutes. The LabelHash algorithm is available through a web server and as a suite of standalone programs a

Methods for Identifying Variation in Large-Scale Genomic Data

The rise of next-generation sequencing has produced an abundance of data with almost limitless analysis applications. As sequencing technology decreases in cost and increases in throughput, the amount of available data is quickly outpacing improve- ments in processor speed. Analysis methods must also increase in scale to remain computationally tractable. At the same time, larger datasets and the availability of population-wide data offer a broader context with which to improve accuracy. This thesis presents three tools that improve the scalability of sequencing data storage and analysis. First, a lossy compression method for RNA-seq alignments offers extreme size reduction without compromising downstream accuracy of isoform assembly and quantitation. Second, I describe a graph genome analysis tool that filters population variants for optimal aligner performance. Finally, I offer several methods for improving CNV segmentation accuracy, including borrowing strength across samples to overcome the limitations of low coverage. These methods compose a practical toolkit for improving the computational power of genomic analysis

A Single-Cell Immune Map of Normal and Cancerous Breast Reveals an Expansion of Phenotypic States Driven by the Tumor Microenvironment

Knowledge of the phenotypic states of immune cells in the tumor microenvironment is essential to understand immunological mechanisms of cancer progression, responses to cancer immunotherapy, and the development of novel rational treatments. Yet, this knowledge is opaque to traditional bulk sequencing methods, and novel single-cell RNA sequencing (scRNA-seq) methods which could potentially address these questions introduce complex patterns of error into data that are poorly characterized. This dissertation describes a computational framework, SEQC, built to facilitate rapid and agile analysis of scRNA-seq approaches that utilize molecular barcodes. It combines SEQC with a clustering and normalization method, BISCUIT, and approaches to examine phenotypic diversity and gene variation. These methods are applied to address the unique computational challenges inherent to analysis of single-cell RNA-seq data derived from multiple patients with diverse phenotypes. This dissertation describes an experiment comprising scRNA-seq of over 47,000 immune cells collected from primary breast carcinomas, matched normal breast tissue, peripheral blood, and using these computational approaches. This atlas revealed significant similarity between normal and tumor tissue resident immune cells. However, it also describes continuous tumor-specific phenotypic expansions driven by distinct environmental cues. These results argue against discrete activation states in T cells and the polarization model of macrophage activation in cancer, and have important implications for characterizing tumor-infiltrating immune cells

Genomic epidemiology of community-associated Staphylococcus aureus in northern Australia and Papua New Guinea

Eike Steinig investigated the transmission dynamics of community-associated MRSA in Far North Queensland and Papua New Guinea. He developed bioinformatics methods based on portable nanopore sequencing technology to infer epidemiological outbreak parameters and clinical genotypes of bacterial pathogens, with applications in rural and remote hospitals that lack sequencing infrastructure

Statistical Population Genomics

This open access volume presents state-of-the-art inference methods in population genomics, focusing on data analysis based on rigorous statistical techniques. After introducing general concepts related to the biology of genomes and their evolution, the book covers state-of-the-art methods for the analysis of genomes in populations, including demography inference, population structure analysis and detection of selection, using both model-based inference and simulation procedures. Last but not least, it offers an overview of the current knowledge acquired by applying such methods to a large variety of eukaryotic organisms. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, pointers to the relevant literature, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Statistical Population Genomics aims to promote and ensure successful applications of population genomic methods to an increasing number of model systems and biological questions

The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Prediction

With the advent of deep learning, research in many areas of machine learning is converging towards the same set of methods and models. For example, long short-term memory networks are not only popular for various tasks in natural language processing (NLP) such as speech recognition, machine translation, handwriting recognition, syntactic parsing, etc., but they are also applicable to seemingly unrelated fields such as robot control, time series prediction, and bioinformatics. Recent advances in contextual word embeddings like BERT boast with achieving state-of-the-art results on 11 NLP tasks with the same model. Before deep learning, a speech recognizer and a syntactic parser used to have little in common as systems were much more tailored towards the task at hand. At the core of this development is the tendency to view each task as yet another data mapping problem, neglecting the particular characteristics and (soft) requirements tasks often have in practice. This often goes along with a sharp break of deep learning methods with previous research in the specific area. This work can be understood as an antithesis to this paradigm. We show how traditional symbolic statistical machine translation models can still improve neural machine translation (NMT) while reducing the risk for common pathologies of NMT such as hallucinations and neologisms. Other external symbolic models such as spell checkers and morphology databases help neural grammatical error correction. We also focus on language models that often do not play a role in vanilla end-to-end approaches and apply them in different ways to word reordering, grammatical error correction, low-resource NMT, and document-level NMT. Finally, we demonstrate the benefit of hierarchical models in sequence-to-sequence prediction. Hand-engineered covering grammars are effective in preventing catastrophic errors in neural text normalization systems. Our operation sequence model for interpretable NMT represents translation as a series of actions that modify the translation state, and can also be seen as derivation in a formal grammar.EPSRC grant EP/L027623/1 EPSRC Tier-2 capital grant EP/P020259/

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

Galaxy Masses

Galaxy masses play a fundamental role in our understanding of structure formation models. This review addresses the variety and reliability of mass estimators that pertain to stars, gas, and dark matter. The different sections on masses from stellar populations, dynamical masses of gas-rich and gas-poor galaxies, with some attention paid to our Milky Way, and masses from weak and strong lensing methods, all provide review material on galaxy masses in a self-consistent manner.Comment: 145 pages, 28 figures, to appear in Reviews of Modern Physics. Figure 22 is missing here, and Figs. 15, 26-28 are at low resolution. This version has a slightly different title and some typos fixed in Chapter 5. For the full review with figures, please consult: http://www.astro.queensu.ca/~courteau/GalaxyMasses_28apr2014.pd