Scalable noninvasive amplicon-based precision sequencing (SNAPseq) for genetic diagnosis and screening of β-thalassemia and sickle cell disease using a next-generation sequencing platform

β-hemoglobinopathies such as β-thalassemia (BT) and Sickle cell disease (SCD) are inherited monogenic blood disorders with significant global burden. Hence, early and affordable diagnosis can alleviate morbidity and reduce mortality given the lack of effective cure. Currently, Sanger sequencing is considered to be the gold standard genetic test for BT and SCD, but it has a very low throughput requiring multiple amplicons and more sequencing reactions to cover the entire HBB gene. To address this, we have demonstrated an extraction-free single amplicon-based approach for screening the entire β-globin gene with clinical samples using Scalable noninvasive amplicon-based precision sequencing (SNAPseq) assay catalyzing with next-generation sequencing (NGS). We optimized the assay using noninvasive buccal swab samples and simple finger prick blood for direct amplification with crude lysates. SNAPseq demonstrates high sensitivity and specificity, having a 100% agreement with Sanger sequencing. Furthermore, to facilitate seamless reporting, we have created a much simpler automated pipeline with comprehensive resources for pathogenic mutations in BT and SCD through data integration after systematic classification of variants according to ACMG and AMP guidelines. To the best of our knowledge, this is the first report of the NGS-based high throughput SNAPseq approach for the detection of both BT and SCD in a single assay with high sensitivity in an automated pipeline

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

GENCODE: reference annotation for the human and mouse genomes in 2023.

GENCODE produces high quality gene and transcript annotation for the human and mouse genomes. All GENCODE annotation is supported by experimental data and serves as a reference for genome biology and clinical genomics. The GENCODE consortium generates targeted experimental data, develops bioinformatic tools and carries out analyses that, along with externally produced data and methods, support the identification and annotation of transcript structures and the determination of their function. Here, we present an update on the annotation of human and mouse genes, including developments in the tools, data, analyses and major collaborations which underpin this progress. For example, we report the creation of a set of non-canonical ORFs identified in GENCODE transcripts, the LRGASP collaboration to assess the use of long transcriptomic data to build transcript models, the progress in collaborations with RefSeq and UniProt to increase convergence in the annotation of human and mouse protein-coding genes, the propagation of GENCODE across the human pan-genome and the development of new tools to support annotation of regulatory features by GENCODE. Our annotation is accessible via Ensembl, the UCSC Genome Browser and https://www.gencodegenes.org

Energy Consumptions for Vehicles using Multitask Learning

This thesis aims to predict energy (fossil fuel and electric) consumption of internal combustion and hybrid vehicles. This thesis is in association with Wireless cars. Accurate prediction of energy consumption in vehicles is vital, as it can pave the way for a more sustainable future. Despite its criticality, accurate predictions of energy consumption are a challenging task. Several factors which impact energy consumption, i.e., average speed, trip duration, etc. , are not available at the beginning of the trip. To use such kinds of features to the full extent, we will be using multitask learning methods. The dataset provided by the company covers different aspects, including GPS information, energy consumption, time, and vehicle configurations which suggests multitask learning as an intriguing technique to approach it. Multitask learning uses a shared feature space wherein information is shared between multiple relevant tasks, helping to predict energy consumption accurately.  Multitask learning (MTL) is susceptible to two crucial issues, namely task dominance and conflicting gradients between different tasks. Previous studies have addressed these issues separately , but we propose a unified framework to tackle these problems simultaneously in this thesis. In the proposed framework we are addressing the issue of task dominance model using Gradient Normalization (GradNorm)  while the issue of conflicting gradients is solved using the Projecting conflicting gradient (PCGrad) technique. Experimental results have shown the success of this method in comparison with other state-of-the-art methods. Apart from creating unified architecture, we are also analyzing the behavioral pattern of the MTL model. This experiment was performed to check which tasks provide the maximum contribution to help improve the overall performance. Apart from the two contributions, we have also performed an additional experiment of task dominance analysis where we have given an equal budget to the main task and also to the auxiliary tasks. The motivation to perform this experiment is to create a main task dominant MTL model, which can take advantage of multitask learning, and improve the performance of the main task simultaneously.  All the novelties presented in this thesis indicate the potential of multitask learning techniques and their future applicability in the vehicular domain

Energy Consumptions for Vehicles using Multitask Learning

This thesis aims to predict energy (fossil fuel and electric) consumption of internal combustion and hybrid vehicles. This thesis is in association with Wireless cars. Accurate prediction of energy consumption in vehicles is vital, as it can pave the way for a more sustainable future. Despite its criticality, accurate predictions of energy consumption are a challenging task. Several factors which impact energy consumption, i.e., average speed, trip duration, etc. , are not available at the beginning of the trip. To use such kinds of features to the full extent, we will be using multitask learning methods. The dataset provided by the company covers different aspects, including GPS information, energy consumption, time, and vehicle configurations which suggests multitask learning as an intriguing technique to approach it. Multitask learning uses a shared feature space wherein information is shared between multiple relevant tasks, helping to predict energy consumption accurately.  Multitask learning (MTL) is susceptible to two crucial issues, namely task dominance and conflicting gradients between different tasks. Previous studies have addressed these issues separately , but we propose a unified framework to tackle these problems simultaneously in this thesis. In the proposed framework we are addressing the issue of task dominance model using Gradient Normalization (GradNorm)  while the issue of conflicting gradients is solved using the Projecting conflicting gradient (PCGrad) technique. Experimental results have shown the success of this method in comparison with other state-of-the-art methods. Apart from creating unified architecture, we are also analyzing the behavioral pattern of the MTL model. This experiment was performed to check which tasks provide the maximum contribution to help improve the overall performance. Apart from the two contributions, we have also performed an additional experiment of task dominance analysis where we have given an equal budget to the main task and also to the auxiliary tasks. The motivation to perform this experiment is to create a main task dominant MTL model, which can take advantage of multitask learning, and improve the performance of the main task simultaneously.  All the novelties presented in this thesis indicate the potential of multitask learning techniques and their future applicability in the vehicular domain

Association between root growth angle and root length density of a near-isogenic line of IR64 rice with DEEPER ROOTING 1 under different levels of soil compaction

DEEPER ROOTING 1 (DRO1) of rice controls the gravitropic response of root growth angle. In order to clarify the effects of DRO1 on root growth angle and root length density under different soil resistance to penetration, and to quantify the relationship between root growth angle and root length density, we assessed the root growth of Dro1-NIL (a near-isogenic line homozygous for the Kinandang Patong allele of DRO1 in the IR64 background) under upland Andosol field conditions in Japan in 2013 and 2014. The trial included three levels of soil compaction (none, moderate, and hard). Root length density at a depth of 30 to 60 cm was largest in Kinandang Patong, followed by Dro1-NIL, and was least in IR64 in both years and in all compaction treatments. Root length density at this depth decreased with hard compaction (to 70% of control) and increased with moderate compaction (to 135%). The number of roots with a deep angle (i.e. 45° to 90° from the horizontal) measured by the basket method was similar at maximum tillering and maturity stages, and its value as a proportion of the total number of roots was strongly correlated with the root length density at 30 to 60 cm in both years, which demonstrates the importance of a deep root angle for the development of deep roots. Dro1-NIL had a higher proportion of deep roots than IR64, but the difference was small under hard compaction, with a significant genotype × compaction interaction

Association between root growth angle and root length density of a near-isogenic line of IR64 rice with <i>DEEPER ROOTING 1</i> under different levels of soil compaction

<p><i>DEEPER ROOTING 1</i> (<i>DRO1</i>) of rice controls the gravitropic response of root growth angle. In order to clarify the effects of <i>DRO1</i> on root growth angle and root length density under different soil resistance to penetration, and to quantify the relationship between root growth angle and root length density, we assessed the root growth of Dro1-NIL (a near-isogenic line homozygous for the Kinandang Patong allele of <i>DRO1</i> in the IR64 background) under upland Andosol field conditions in Japan in 2013 and 2014. The trial included three levels of soil compaction (none, moderate, and hard). Root length density at a depth of 30 to 60 cm was largest in Kinandang Patong, followed by Dro1-NIL, and was least in IR64 in both years and in all compaction treatments. Root length density at this depth decreased with hard compaction (to 70% of control) and increased with moderate compaction (to 135%). The number of roots with a deep angle (i.e. 45° to 90° from the horizontal) measured by the basket method was similar at maximum tillering and maturity stages, and its value as a proportion of the total number of roots was strongly correlated with the root length density at 30 to 60 cm in both years, which demonstrates the importance of a deep root angle for the development of deep roots. Dro1-NIL had a higher proportion of deep roots than IR64, but the difference was small under hard compaction, with a significant genotype × compaction interaction.</p