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Article ; Online: SAMStat 2: quality control for next generation sequencing data.

Lassmann, Timo

2023 Volume 39, Issue 1

Abstract: Motivation: SAMStat is an efficient program to extract quality control metrics from fastq and SAM/BAM files. A distinguishing feature is that it displays sequence composition, base quality composition and mapping error profiles split by mapping quality. ...

Abstract	Motivation: SAMStat is an efficient program to extract quality control metrics from fastq and SAM/BAM files. A distinguishing feature is that it displays sequence composition, base quality composition and mapping error profiles split by mapping quality. This allows users to rapidly identify reasons for poor mapping including the presence of untrimmed adapters or poor sequencing quality at individual read positions. Results: Here, we present a major update to SAMStat. The new version now supports paired-end and long-read data. Quality control plots are drawn using the ploty javascript library. Availability and implementation: The source code of SAMStat and code to reproduce the results are found here: https://github.com/timolassmann/samstat.
MeSH term(s)	High-Throughput Nucleotide Sequencing ; Software ; Quality Control ; Base Composition ; Sequence Analysis, DNA/methods
Language	English
Publishing date	2023-01-25
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btad019
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Article ; Online: Kalign 3: multiple sequence alignment of large data sets.

Lassmann, Timo

Bioinformatics (Oxford, England)

2019

Abstract: Motivation: Kalign is an efficient multiple sequence alignment (MSA) program capable of aligning thousands of protein or nucleotide sequences. However, current alignment problems involving large numbers of sequences are exceeding Kalign's original ... ...

Abstract	Motivation: Kalign is an efficient multiple sequence alignment (MSA) program capable of aligning thousands of protein or nucleotide sequences. However, current alignment problems involving large numbers of sequences are exceeding Kalign's original design specifications. Here we present a completely re-written and updated version to meet current and future alignment challenges. Results: Kalign now uses a SIMD accelerated version of the bit-parallel Gene Myers algorithm to estimate pariwise distances, adopts a sequence embedding strategy and the bi-secting K-means algorithm to rapidly construct guide trees for thousands of sequences. The new version maintains high alignment accuracy on both protein and nucleotide alignments and scales better than other MSA tools. Availability: The source code of Kalign and code to reproduce the results are found here: https://github.com/timolassmann/kalign.
Language	English
Publishing date	2019-10-26
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btz795
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Article ; Online: An expanded phenotype centric benchmark of variant prioritisation tools.

Anderson, Denise / Lassmann, Timo

Human mutation

2022 Volume 43, Issue 5, Page(s) 539–546

Abstract: Identifying the causal variant for diagnosis of genetic diseases is challenging when using next-generation sequencing approaches and variant prioritization tools can assist in this task. These tools provide in silico predictions of variant pathogenicity, ...

Abstract	Identifying the causal variant for diagnosis of genetic diseases is challenging when using next-generation sequencing approaches and variant prioritization tools can assist in this task. These tools provide in silico predictions of variant pathogenicity, however they are agnostic to the disease under study. We previously performed a disease-specific benchmark of 24 such tools to assess how they perform in different disease contexts. We found that the tools themselves show large differences in performance, but more importantly that the best tools for variant prioritization are dependent on the disease phenotypes being considered. Here we expand the assessment to 37 tools and refine our assessment by separating performance for nonsynonymous single nucleotide variants (nsSNVs) and missense variants (i.e., excluding nonsense variants). We found differences in performance for missense variants compared to nsSNVs and recommend three tools that stand out in terms of their performance (BayesDel, CADD, and ClinPred).
MeSH term(s)	Benchmarking ; Computational Biology ; High-Throughput Nucleotide Sequencing ; Humans ; Mutation, Missense ; Phenotype
Language	English
Publishing date	2022-03-09
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1126646-6
ISSN	1098-1004 ; 1059-7794
ISSN (online)	1098-1004
ISSN	1059-7794
DOI	10.1002/humu.24362
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Article ; Online: TagDust2: a generic method to extract reads from sequencing data.

Lassmann, Timo

BMC bioinformatics

2015 Volume 16, Page(s) 24

Abstract: Background: Arguably the most basic step in the analysis of next generation sequencing data (NGS) involves the extraction of mappable reads from the raw reads produced by sequencing instruments. The presence of barcodes, adaptors and artifacts subject ... ...

Abstract	Background: Arguably the most basic step in the analysis of next generation sequencing data (NGS) involves the extraction of mappable reads from the raw reads produced by sequencing instruments. The presence of barcodes, adaptors and artifacts subject to sequencing errors makes this step non-trivial. Results: Here I present TagDust2, a generic approach utilizing a library of hidden Markov models (HMM) to accurately extract reads from a wide array of possible read architectures. TagDust2 extracts more reads of higher quality compared to other approaches. Processing of multiplexed single, paired end and libraries containing unique molecular identifiers is fully supported. Two additional post processing steps are included to exclude known contaminants and filter out low complexity sequences. Finally, TagDust2 can automatically detect the library type of sequenced data from a predefined selection. Conclusion: Taken together TagDust2 is a feature rich, flexible and adaptive solution to go from raw to mappable NGS reads in a single step. The ability to recognize and record the contents of raw reads will help to automate and demystify the initial, and often poorly documented, steps in NGS data analysis pipelines. TagDust2 is freely available at: http://tagdust.sourceforge.net .
MeSH term(s)	Automatic Data Processing ; Computational Biology/methods ; Gene Library ; High-Throughput Nucleotide Sequencing/methods ; Humans ; Sequence Analysis, DNA/methods ; Software
Language	English
Publishing date	2015-01-28
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-015-0454-y
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Article ; Online: A phenotype centric benchmark of variant prioritisation tools.

Anderson, Denise / Lassmann, Timo

NPJ genomic medicine

2018 Volume 3, Page(s) 5

Abstract: Next generation sequencing is a standard tool used in clinical diagnostics. In Mendelian diseases the challenge is to discover the single etiological variant among thousands of benign or functionally unrelated variants. After calling variants from ... ...

Abstract	Next generation sequencing is a standard tool used in clinical diagnostics. In Mendelian diseases the challenge is to discover the single etiological variant among thousands of benign or functionally unrelated variants. After calling variants from aligned sequencing reads, variant prioritisation tools are used to examine the conservation or potential functional consequences of variants. We hypothesised that the performance of variant prioritisation tools may vary by disease phenotype. To test this we created benchmark data sets for variants associated with different disease phenotypes. We found that performance of 24 tested tools is highly variable and differs by disease phenotype. The task of identifying a causative variant amongst a large number of benign variants is challenging for all tools, highlighting the need for further development in the field. Based on our observations, we recommend use of five top performers found in this study (FATHMM, M-CAP, MetaLR, MetaSVM and VEST3). In addition we provide tables indicating which analytical approach works best in which disease context. Variant prioritisation tools are best suited to investigate variants associated with well-studied genetic diseases, as these variants are more readily available during algorithm development than variants associated with rare diseases. We anticipate that further development into disease focussed tools will lead to significant improvements.
Language	English
Publishing date	2018-02-05
Publishing country	England
Document type	Journal Article
ZDB-ID	2813848-X
ISSN	2056-7944 ; 2056-7944
ISSN (online)	2056-7944
ISSN	2056-7944
DOI	10.1038/s41525-018-0044-9
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Article ; Online: CRISPR-Cas9-generated PTCHD1 2489T>G stem cells recapitulate patient phenotype when undergoing neural induction.

Farley, Kathryn O / Forbes, Catherine A / Shaw, Nicole C / Kuzminski, Emma / Ward, Michelle / Baynam, Gareth / Lassmann, Timo / Fear, Vanessa S

HGG advances

2023 Volume 5, Issue 1, Page(s) 100257

Abstract: An estimated 3.5%-5.9% of the global population live with rare diseases, and approximately 80% of these diseases have a genetic cause. Rare genetic diseases are difficult to diagnose, with some affected individuals experiencing diagnostic delays of 5-30 ... ...

Abstract	An estimated 3.5%-5.9% of the global population live with rare diseases, and approximately 80% of these diseases have a genetic cause. Rare genetic diseases are difficult to diagnose, with some affected individuals experiencing diagnostic delays of 5-30 years. Next-generation sequencing has improved clinical diagnostic rates to 33%-48%. In a majority of cases, novel variants potentially causing the disease are discovered. These variants require functional validation in specialist laboratories, resulting in a diagnostic delay. In the interim, the finding is classified as a genetic variant of uncertain significance (VUS) and the affected individual remains undiagnosed. A VUS (PTCHD1 c. 2489T>G) was identified in a child with autistic behavior, global developmental delay, and hypotonia. Loss of function mutations in PTCHD1 are associated with autism spectrum disorder and intellectual disability; however, the molecular function of PTCHD1 and its role in neurodevelopmental disease is unknown. Here, we apply CRISPR gene editing and induced pluripotent stem cell (iPSC) neural disease modeling to assess the variant. During differentiation from iPSCs to neural progenitors, we detect subtle but significant gene signatures in synaptic transmission and muscle contraction pathways. Our work supports the causal link between the genetic variant and the child's phenotype, providing evidence for the variant to be considered a pathogenic variant according to the American College of Medical Genetics and Genomics guidelines. In addition, our study provides molecular data on the role of PTCHD1 in the context of other neurodevelopmental disorders.
MeSH term(s)	Child ; Humans ; Autism Spectrum Disorder/diagnosis ; CRISPR-Cas Systems/genetics ; Delayed Diagnosis ; Phenotype ; Stem Cells/metabolism ; Membrane Proteins/genetics
Chemical Substances	PTCHD1 protein, human ; Membrane Proteins
Language	English
Publishing date	2023-11-24
Publishing country	United States
Document type	Journal Article
ISSN	2666-2477
ISSN (online)	2666-2477
DOI	10.1016/j.xhgg.2023.100257
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Article ; Online: Personalised analytics for rare disease diagnostics.

Anderson, Denise / Baynam, Gareth / Blackwell, Jenefer M / Lassmann, Timo

Nature communications

2019 Volume 10, Issue 1, Page(s) 5274

Abstract: Whole genome and exome sequencing is a standard tool for the diagnosis of patients suffering from rare and other genetic disorders. The interpretation of the tens of thousands of variants returned from such tests remains a major challenge. Here we focus ... ...

Abstract	Whole genome and exome sequencing is a standard tool for the diagnosis of patients suffering from rare and other genetic disorders. The interpretation of the tens of thousands of variants returned from such tests remains a major challenge. Here we focus on the problem of prioritising variants with respect to the observed disease phenotype. We hypothesise that linking patterns of gene expression across multiple tissues to the phenotypes will aid in discovering disease causing variants. To test this, we construct classifiers that learn associations between tissue-specific gene expression and disease phenotypes. We find that using Genotype-Tissue Expression project (GTEx) expression data in conjunction with disease agnostic variant prioritisation methods (CADD or MetaSVM) results in consistent improvements in classification accuracy. Our method represents a previously overlooked avenue of utilising existing expression data for clinical diagnostics, and also opens the door to use of other functional genomic data sets in the same manner.
MeSH term(s)	Gene Expression Profiling ; Gene Expression Regulation ; Genetic Variation ; Genome, Human/genetics ; Genome-Wide Association Study/methods ; Genotype ; High-Throughput Nucleotide Sequencing/methods ; Humans ; Phenotype ; Precision Medicine/methods ; Rare Diseases/diagnosis ; Rare Diseases/genetics ; Exome Sequencing/methods
Language	English
Publishing date	2019-11-21
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-019-13345-5
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Expression Levels of Therapeutic Targets as Indicators of Sensitivity to Targeted Therapeutics.

Roy, Riti / Winteringham, Louise N / Lassmann, Timo / Forrest, Alistair R R

Molecular cancer therapeutics

2019 Volume 18, Issue 12, Page(s) 2480–2489

Abstract: Cancer precision medicine aims to predict the drug likely to yield the best response for a patient. Genomic sequencing of tumors is currently being used to better inform treatment options; however, this approach has had a limited clinical impact due to ... ...

Abstract	Cancer precision medicine aims to predict the drug likely to yield the best response for a patient. Genomic sequencing of tumors is currently being used to better inform treatment options; however, this approach has had a limited clinical impact due to the paucity of actionable mutations. An alternative to mutation status is the use of gene expression signatures to predict response. Using data from two large-scale studies, The Genomics of Drug Sensitivity of Cancer (GDSC) and The Cancer Therapeutics Response Portal (CTRP), we investigated the relationship between the sensitivity of hundreds of cell lines to hundreds of drugs, and the relative expression levels of the targets these drugs are directed against. For approximately one third of the drugs considered (73/222 in GDSC and 131/360 in CTRP), sensitivity was significantly correlated with the expression of at least one of the known targets. Surprisingly, for 8% of the annotated targets, there was a significant anticorrelation between target expression and sensitivity. For several cases, this corresponded to drugs targeting multiple genes in the same family, with the expression of one target significantly correlated with sensitivity and another significantly anticorrelated suggesting a possible role in resistance. Furthermore, we identified nontarget genes that are significantly correlated or anticorrelated with drug sensitivity, and find literature linking several to sensitization and resistance. Our analyses provide novel and important insights into both potential mechanisms of resistance and relative efficacy of drugs against the same target.
MeSH term(s)	Gene Expression/genetics ; Humans ; Precision Medicine ; Sensitivity and Specificity
Language	English
Publishing date	2019-08-29
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2063563-1
ISSN	1538-8514 ; 1535-7163
ISSN (online)	1538-8514
ISSN	1535-7163
DOI	10.1158/1535-7163.MCT-19-0273
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Article: Functional validation of variants of unknown significance using CRISPR gene editing and transcriptomics: A Kleefstra syndrome case study

Fear, Vanessa S / Forbes, Catherine A / Anderson, Denise / Rauschert, Sebastian / Syn, Genevieve / Shaw, Nicole / Jones, Matthew E / Forrest, Alistair RR / Baynam, Gareth / Lassmann, Timo

Gene. 2022 May 05, v. 821

2022

Abstract: There are an estimated > 400 million people living with a rare disease globally, with genetic variants the cause of approximately 80% of cases. Next Generation Sequencing (NGS) rapidly identifies genetic variants however they are often of unknown ... ...

Abstract	There are an estimated > 400 million people living with a rare disease globally, with genetic variants the cause of approximately 80% of cases. Next Generation Sequencing (NGS) rapidly identifies genetic variants however they are often of unknown significance. Low throughput functional validation in specialist laboratories is the current ad hoc approach for functional validation of genetic variants, which creating major bottlenecks in patient diagnosis. This study investigates the application of CRISPR gene editing followed by genome wide transcriptomic profiling to facilitate patient diagnosis. As proof-of-concept, we introduced a variant in the Euchromatin histone methyl transferase (EHMT1) gene into HEK293T cells. We identified changes in the regulation of the cell cycle, neural gene expression and suppression of gene expression changes on chromosome 19 and chromosome X, that are in keeping with Kleefstra syndrome clinical phenotype and/or provide insight into disease mechanism. This study demonstrates the utility of genome editing followed by functional readouts to rapidly and systematically validating the function of variants of unknown significance in patients suffering from rare diseases.
Keywords	case studies ; cell cycle ; chromosomes ; gene expression ; genes ; histones ; patients ; phenotype ; transcriptomics
Language	English
Dates of publication	2022-0505
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	391792-7
ISSN	1879-0038 ; 0378-1119
ISSN (online)	1879-0038
ISSN	0378-1119
DOI	10.1016/j.gene.2022.146287
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Gene editing and cardiac disease modelling for the interpretation of genetic variants of uncertain significance in congenital heart disease.

Fear, Vanessa S / Forbes, Catherine A / Shaw, Nicole C / Farley, Kathryn O / Mantegna, Jessica L / Htun, Jasmin P / Syn, Genevieve / Viola, Helena / Cserne Szappanos, Henrietta / Hool, Livia / Ward, Michelle / Baynam, Gareth / Lassmann, Timo

Stem cell research & therapy

2023 Volume 14, Issue 1, Page(s) 345

Abstract: Background: Genomic sequencing in congenital heart disease (CHD) patients often discovers novel genetic variants, which are classified as variants of uncertain significance (VUS). Functional analysis of each VUS is required in specialised laboratories, ... ...

Abstract	Background: Genomic sequencing in congenital heart disease (CHD) patients often discovers novel genetic variants, which are classified as variants of uncertain significance (VUS). Functional analysis of each VUS is required in specialised laboratories, to determine whether the VUS is disease causative or not, leading to lengthy diagnostic delays. We investigated stem cell cardiac disease modelling and transcriptomics for the purpose of genetic variant classification using a GATA4 (p.Arg283Cys) VUS in a patient with CHD. Methods: We performed high efficiency CRISPR gene editing with homology directed repair in induced pluripotent stem cells (iPSCs), followed by rapid clonal selection with amplicon sequencing. Genetic variant and healthy matched control cells were compared using cardiomyocyte disease modelling and transcriptomics. Results: Genetic variant and healthy cardiomyocytes similarly expressed Troponin T (cTNNT), and GATA4. Transcriptomics analysis of cardiomyocyte differentiation identified changes consistent with the patient's clinical human phenotype ontology terms. Further, transcriptomics revealed changes in calcium signalling, and cardiomyocyte adrenergic signalling in the variant cells. Functional testing demonstrated, altered action potentials in GATA4 genetic variant cardiomyocytes were consistent with patient cardiac abnormalities. Conclusions: This work provides in vivo functional studies supportive of a damaging effect on the gene or gene product. Furthermore, we demonstrate the utility of iPSCs, CRISPR gene editing and cardiac disease modelling for genetic variant interpretation. The method can readily be applied to other genetic variants in GATA4 or other genes in cardiac disease, providing a centralised assessment pathway for patient genetic variant interpretation.
MeSH term(s)	Humans ; Gene Editing ; Heart Defects, Congenital/genetics ; Heart Defects, Congenital/metabolism ; Myocytes, Cardiac/metabolism ; Base Sequence ; Signal Transduction
Language	English
Publishing date	2023-12-05
Publishing country	England
Document type	Journal Article
ZDB-ID	2548671-8
ISSN	1757-6512 ; 1757-6512
ISSN (online)	1757-6512
ISSN	1757-6512
DOI	10.1186/s13287-023-03592-1
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