The Efficacy of Whole-Genome Sequencing in the Diagnosis Of Complex Neurological Phenotypes

Whole-genome sequencing (WGS) is being increasingly utilized for the diagnosis of neurological disease. The advent of next-generation sequencing (NGS) has replaced Sanger sequencing due to its ability to sequence millions of fragments in parallel, in real-time. It’s application in targeted gene panels and whole-exome sequencing (WES) has revolutionized standard investigation practices of neurodevelopmental diseases (NDDs). WGS utilizes NGS technology in order to sequence beyond the exome and into the remaining 98-99% of the genetic code comprising the genome. In addition to increased coverage, WGS allows for the detection of novel gene variants, copy number variants (CNVs) and single nucleotide variants (SNVs) that are not traditionally picked up by WES. Furthermore, RNA sequencing (RNA-Seq) of the blood used in conjunction with WGS may have the ability to validate WGS findings by analyzing gene expression in addition to identifying novel RNA species within the transcriptome. The objective of this retrospective study was to measure the diagnostic yield of trio-based WGS and RNA-Seq against that of negative or inconclusive WES in a patient cohort comprised of complex neurological phenotypes. Whole genome sequencing was performed by Medical Neurogenetics LLC, a CLIA-certified laboratory in Atlanta, Georgia. This laboratory utilized the Illumina NovaSeq 6000 Sequencing System, with a goal of 30x coverage of 99% of mapped genome regions. Alignment and variant interpretation was performed by Dragen v2.2 and CNV analysis by Dragen v2.5. Variants were assessed in accordance with current ACMG criteria. WGS with complementary RNA-Seq resulted in 7 solved patient cases, providing a 31.8% yield. This phenotypically complex cohort was comprised of a spectrum of neurological conditions with suspected underlying genetic mechanisms. The use of WGS in conjunction with RNA-Seq resulted in a markedly increased diagnostic yield over that of preceding WES and conventional first-tier tests which included chromosomal microarray, targeted gene panels, and metabolic testing. Thus, proving its efficacy in the clinical setting

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

PennCNV in whole-genome sequencing data

Abstract Background The use of high-throughput sequencing data has improved the results of genomic analysis due to the resolution of mapping algorithms. Although several tools for copy-number variation calling in whole genome sequencing have been published, the noisy nature of sequencing data is still a limitation for accuracy and concordance among such tools. To assess the performance of PennCNV original algorithm for array data in whole genome sequencing data, we processed mapping (BAM) files to extract coverage, representing log R ratio (LRR) of signal intensity, and B allele frequency (BAF). Results We used high quality sample NA12878 from the recently reported NIST database and created 10 artificial samples with several CNVs spread along all chromosomes. We compared PennCNV-Seq with other tools with general deletions and duplications, as well as for different number of copies and copy-neutral loss-of-heterozygosity (LOH). Conclusion PennCNV-Seq was able to find correct CNVs and can be integrated in existing CNV calling pipelines to report accurately the number of copies in specific genomic regions