Predicting the impact of single nucleotide variants on splicing via sequence‐based deep neural networks and genomic features

AbstractDespite deep neural networks (DNNs) having found great success at improving performance on various prediction tasks in computational genomics, it remains difficult to understand why they make any given prediction. In genomics, the main approaches to interpret a high-performing DNN are to visualize learned representations via weight visualizations and attribution methods. While these methods can be informative, each has strong limitations. For instance, attribution methods only uncover the independent contribution of single nucleotide variants in a given sequence. Here we discuss and argue for global importance analysis which can quantify population-level importance of putative features and their interactions learned by a DNN. We highlight recent work that has benefited from this interpretability approach and then discuss connections between global importance analysis and causality.

Download Full-text

IoT-Based Bee Swarm Activity Acoustic Classification Using Deep Neural Networks

Sensors ◽

10.3390/s21030676 ◽

2021 ◽

Vol 21 (3) ◽

pp. 676

Author(s):

Andrej Zgank

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Markov Models ◽

Audio Signal ◽

Audio Signals ◽

Mel Frequency Cepstral Coefficients ◽

Animal Activity ◽

The Impact ◽

Acoustic Classification ◽

Swarm Activity

Animal activity acoustic monitoring is becoming one of the necessary tools in agriculture, including beekeeping. It can assist in the control of beehives in remote locations. It is possible to classify bee swarm activity from audio signals using such approaches. A deep neural networks IoT-based acoustic swarm classification is proposed in this paper. Audio recordings were obtained from the Open Source Beehive project. Mel-frequency cepstral coefficients features were extracted from the audio signal. The lossless WAV and lossy MP3 audio formats were compared for IoT-based solutions. An analysis was made of the impact of the deep neural network parameters on the classification results. The best overall classification accuracy with uncompressed audio was 94.09%, but MP3 compression degraded the DNN accuracy by over 10%. The evaluation of the proposed deep neural networks IoT-based bee activity acoustic classification showed improved results if compared to the previous hidden Markov models system.

Download Full-text

Prediction of genome-wide effects of single nucleotide variants on transcription factor binding

Scientific Reports ◽

10.1038/s41598-020-74793-4 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Sebastian Carrasco Pro ◽

Katia Bulekova ◽

Brian Gregor ◽

Adam Labadorf ◽

Juan Ignacio Fuxman Bass

Keyword(s):

Binding Sites ◽

Cancer Type ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Regulatory Regions ◽

Genome Wide ◽

Transcriptional Regulatory ◽

Gene Regulatory ◽

The Impact ◽

The Relationship

Abstract Single nucleotide variants (SNVs) located in transcriptional regulatory regions can result in gene expression changes that lead to adaptive or detrimental phenotypic outcomes. Here, we predict gain or loss of binding sites for 741 transcription factors (TFs) across the human genome. We calculated ‘gainability’ and ‘disruptability’ scores for each TF that represent the likelihood of binding sites being created or disrupted, respectively. We found that functional cis-eQTL SNVs are more likely to alter TF binding sites than rare SNVs in the human population. In addition, we show that cancer somatic mutations have different effects on TF binding sites from different TF families on a cancer-type basis. Finally, we discuss the relationship between these results and cancer mutational signatures. Altogether, we provide a blueprint to study the impact of SNVs derived from genetic variation or disease association on TF binding to gene regulatory regions.

Download Full-text

Re-evaluation of single nucleotide variants and identification of structural variants in a cohort of 45 sudden unexplained death cases

International Journal of Legal Medicine ◽

10.1007/s00414-021-02580-5 ◽

2021 ◽

Author(s):

Jacqueline Neubauer ◽

Shouyu Wang ◽

Giancarlo Russo ◽

Cordula Haas

Keyword(s):

Sudden Death ◽

Cardiac Diseases ◽

Structural Variants ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Sudden Unexplained Death ◽

Unexplained Death ◽

Pathogenic Variants ◽

The Impact ◽

Death Cases

AbstractSudden unexplained death (SUD) takes up a considerable part in overall sudden death cases, especially in adolescents and young adults. During the past decade, many channelopathy- and cardiomyopathy-associated single nucleotide variants (SNVs) have been identified in SUD studies by means of postmortem molecular autopsy, yet the number of cases that remain inconclusive is still high. Recent studies had suggested that structural variants (SVs) might play an important role in SUD, but there is no consensus on the impact of SVs on inherited cardiac diseases. In this study, we searched for potentially pathogenic SVs in 244 genes associated with cardiac diseases. Whole-exome sequencing and appropriate data analysis were performed in 45 SUD cases. Re-analysis of the exome data according to the current ACMG guidelines identified 14 pathogenic or likely pathogenic variants in 10 (22.2%) out of the 45 SUD cases, whereof 2 (4.4%) individuals had variants with likely functional effects in the channelopathy-associated genes SCN5A and TRDN and 1 (2.2%) individual in the cardiomyopathy-associated gene DTNA. In addition, 18 structural variants (SVs) were identified in 15 out of the 45 individuals. Two SVs with likely functional impairment were found in the coding regions of PDSS2 and TRPM4 in 2 SUD cases (4.4%). Both were identified as heterozygous deletions, which were confirmed by multiplex ligation-dependent probe amplification. In conclusion, our findings support that SVs could contribute to the pathology of the sudden death event in some of the cases and therefore should be investigated on a routine basis in suspected SUD cases.

Download Full-text

CSIG-22. CANCER-ASSOCIATED MISSENSE SINGLE NUCLEOTIDE VARIANTS REGULATE THE STABILITY AND SUBCELLULAR LOCALIZATION OF NF2/MERLIN

Neuro-Oncology ◽

10.1093/neuonc/noaa215.134 ◽

2020 ◽

Vol 22 (Supplement_2) ◽

pp. ii32-ii32

Author(s):

Charlotte Eaton ◽

Paola Bisignano ◽

David Raleigh

Keyword(s):

Tumor Suppressor ◽

Subcellular Localization ◽

Function Analysis ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Ferm Domain ◽

Binding Partners ◽

Subcellular Compartments ◽

The Stability ◽

The Impact

Abstract BACKGROUND Alterations in the NF2 tumor suppressor gene lead to meningiomas and schwannomas, but the tumor suppressor functions of the NF2 gene product, Merlin, are incompletely understood. To address this problem, we performed a structure-function analysis of Merlin by expressing cancer-associated missense single-nucleotide variants (mSNVs) in primary cancer cells for biochemical and cell biology experiments. METHODS All NF2 mSNVs were assembled from cBioPortal and COSMIC, and modelled on the FERM, a-helical, and C-terminal domains of Merlin (PDB 4ZRJ) using comparative structure prediction on the Robetta server and visually inspected using Pymol. mSNV hotspots were defined from sliding windows with at least 10 mutations within 5 residues in either direction. mSNVs from hotspots in meningiomas, schwannomas, or both, were selected for in vitro mechanistic analyses using immunofluorescence and immunoblotting of whole cell, plasma membrane, cytoskeletal, cytoplasmic, nuclear, and chromatin subcellular fractions from M10G meningioma cells and HEI-193 schwannoma cells. RESULTS We identified the following cancer-associated hotspot mSNVs in NF2, which were over-expressed for mechanistic studies: L46R, S156N, W191R, A211D, V219M, R418C and R462K. Endogenous Merlin was detected in all subcellular compartments, but was enriched in the nucleus. L46R and A211D mapped to hydrophobic pockets in the FERM domain, destabilized Merlin, and excluded Merlin from all subcellular compartments except the cytoskeleton. S156N, W191R and V219M also mapped to the FERM domain, but did not affect Merlin stability, and V219M attenuated chromatin localization, suggesting this motif may be involved in binding events that regulate subcellular localization. R418C and R463K mapped to the a-helical domain, but only R418C destabilized Merlin. CONCLUSION Our results suggest that cancer-associated mSNVs inactive the tumor suppressor functions of NF2 by altering the stability, subcellular localization, or binding partners of Merlin. Further work is required to identify and understand the impact of binding partners and subcellular localization on Merlin function.

Download Full-text

Impact of Low Resolution on Image Recognition with Deep Neural Networks: An Experimental Study

International Journal of Applied Mathematics and Computer Science ◽

10.2478/amcs-2018-0056 ◽

2018 ◽

Vol 28 (4) ◽

pp. 735-744 ◽

Cited By ~ 9

Author(s):

Michał Koziarski ◽

Bogusław Cyganek

Keyword(s):

Neural Networks ◽

Image Recognition ◽

Classification Accuracy ◽

Deep Neural Networks ◽

Dynamic Range ◽

Super Resolution ◽

Image Resolution ◽

Quality Data ◽

Low Resolution ◽

The Impact

Abstract Due to the advances made in recent years, methods based on deep neural networks have been able to achieve a state-of-the-art performance in various computer vision problems. In some tasks, such as image recognition, neural-based approaches have even been able to surpass human performance. However, the benchmarks on which neural networks achieve these impressive results usually consist of fairly high quality data. On the other hand, in practical applications we are often faced with images of low quality, affected by factors such as low resolution, presence of noise or a small dynamic range. It is unclear how resilient deep neural networks are to the presence of such factors. In this paper we experimentally evaluate the impact of low resolution on the classification accuracy of several notable neural architectures of recent years. Furthermore, we examine the possibility of improving neural networks’ performance in the task of low resolution image recognition by applying super-resolution prior to classification. The results of our experiments indicate that contemporary neural architectures remain significantly affected by low image resolution. By applying super-resolution prior to classification we were able to alleviate this issue to a large extent as long as the resolution of the images did not decrease too severely. However, in the case of very low resolution images the classification accuracy remained considerably affected.

Download Full-text

RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Genome Biology ◽

10.1186/s13059-019-1847-4 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 5

Author(s):

Hai Lin ◽

Katherine A. Hargreaves ◽

Rudong Li ◽

Jill L. Reiter ◽

Yue Wang ◽

...

Keyword(s):

Rna Splicing ◽

Evolutionary Conservation ◽

Random Forest Classifier ◽

Training Data ◽

Reporter Assay ◽

Single Nucleotide Variants ◽

Excellent Performance ◽

Computational Framework ◽

Single Nucleotide ◽

The Impact

AbstractSingle nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure, and evolutionary conservation features. RegSNPs-intron showed excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of RegSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis.

Download Full-text

Fido-SNP: the first webserver for scoring the impact of single nucleotide variants in the dog genome

Nucleic Acids Research ◽

10.1093/nar/gkz420 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W136-W141 ◽

Cited By ~ 1

Author(s):

Emidio Capriotti ◽

Ludovica Montanucci ◽

Giuseppe Profiti ◽

Ivan Rossi ◽

Diana Giannuzzi ◽

...

Keyword(s):

Matthews Correlation Coefficient ◽

Genomic Variation ◽

Gradient Boosting ◽

Binary Classifier ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Coding Regions ◽

Variation Data ◽

Boosting Algorithm ◽

The Impact

Abstract As the amount of genomic variation data increases, tools that are able to score the functional impact of single nucleotide variants become more and more necessary. While there are several prediction servers available for interpreting the effects of variants in the human genome, only few have been developed for other species, and none were specifically designed for species of veterinary interest such as the dog. Here, we present Fido-SNP the first predictor able to discriminate between Pathogenic and Benign single-nucleotide variants in the dog genome. Fido-SNP is a binary classifier based on the Gradient Boosting algorithm. It is able to classify and score the impact of variants in both coding and non-coding regions based on sequence features within seconds. When validated on a previously unseen set of annotated variants from the OMIA database, Fido-SNP reaches 88% overall accuracy, 0.77 Matthews correlation coefficient and 0.91 Area Under the ROC Curve.

Download Full-text

The Impact of Architecture on the Deep Neural Networks Training

2019 12th International Conference on Human System Interaction (HSI) ◽

10.1109/hsi47298.2019.8942622 ◽

2019 ◽

Author(s):

Pawel Rozycki ◽

Janusz Kolbusz ◽

Aleksander Malinowski ◽

Bogdan Wilamowski

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

The Impact

Download Full-text

Single Nucleotide Variations In Spectrin-1β Accentuate The Red Blood Cell Storage Lesion

Blood ◽

10.1182/blood.v122.21.3422.3422 ◽

2013 ◽

Vol 122 (21) ◽

pp. 3422-3422

Author(s):

Melinda M Dean ◽

Katrina Kildey ◽

Thu V Tran ◽

Kelly Rooks ◽

Shoma Baidya ◽

...

Keyword(s):

Time Course ◽

Conflicts Of Interest ◽

Flow Cytometric Analysis ◽

Osmotic Fragility ◽

Blood Component ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Storage Lesion ◽

Background Strain ◽

The Impact

Abstract Introduction During routine storage packed red blood cells (PRBC) undergo biochemical and biophysical changes collectively referred to as the “RBC storage lesion”. Donor-to-donor variability in the severity of the storage lesion has been reported. The extent to which donor-associated differences in blood component storage affect blood product quality and post-transfusion outcome remains unknown. Murine models with single nucleotide variants (SNV) in gene encoding spectrin-1β were used to investigate the impact of mutations on the RBC storage lesion. Methods Two murine lineages with N-ethyl-N-nitrosourea (ENU) generated single SNV in Spnb1, encoding spectrin-1β (Table 1), were selected from the Australian Phenomics Facility library (http://databases.apf.edu.au/mutations). Using genetic selection, homozygous (HOM), heterozygous (HET) and unaffected (WT) mice from each strain were generated (C57BL/6 background strain). Murine blood was leucoreduced, prepared in SAGM (0.4 HCT) and stored at 4°C for time course assessment of RBC characteristics. At day (D), D2, D7, D14 and D21 of storage, RBC integrity and evidence of storage-related changes were investigated using RBC osmotic fragility and flow cytometric analysis of CD44, CD47, TER119 and phosphatidylserine (PS). Data were generated from analysis of blood from Spnb1 (pedigree spectrin-1β a) homozygous (HOM, n=3), heterozygous (HET, n=3) and unaffected (WT, n=2 ); Spnb1 (pedigree spectrin-1β b) HOM (n=4), HET( n=4); C57BL/6 (n=4). The Mann-Whitney Test and ANOVA were utilised for statistical analyses (95% CI). Results At D2 of storage SNV in Spnb1 did not alter RBC characteristics, with all mice studied demonstrating a similar resistance to osmotic lysis and levels of CD44, CD47, TER119 and PS. By D7 of storage, clear pedigree-related differences in RBC characteristics were evident. At D7, RBC from spectrin-1β(a) HOM mice had significantly increased osmotic fragility and exposure of PS as well as significantly reduced CD44 and TER119 expression compared to unaffected siblings and background strain. Of note, these changes were not evident in the spectrin-1β(b) HOM mice at D7. For both strains at D7, heterozygous SNV did not exhibit altered storage parameters. By D14 both HOM and HET spectrin-1β(a) mice demonstrated a phenotype consistent with an exacerbated RBC storage lesion, characterised by significantly increased osmotic fragility and exposure of PS, and reduced CD44 and CD47 compared to background strain. At D14 there was also evidence of exacerbation of the storage lesion in stored RBC from HOM spectrin-1β(b) mice (significantly increased PS), though this was not to the extent observed in the spectrin-1β(a) mice. By D21 all murine RBC were substantially degraded under these storage conditions. Conclusions SNV in Spnb1,encoding RBC structural protein spectrin-1β, resulted in both early onset and exacerbation of the RBC storage lesion. Further, the degree of storage lesion and the point at which RBC degradation was observed was not only dependent on the homozygous or heterozygous status, but the mutation itself. These data demonstrate that minor genetic variation in genes encoding important RBC proteins contribute to donor related differences in PRBC storage. Disclosures: No relevant conflicts of interest to declare.

Download Full-text