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Biomedical subjects

Timo Lassmann

Publications and source records attributed to Timo Lassmann.

6 recordsLinked to original sources

Functional characterization of the MED12 p.Arg1138Trp variant in females: implications for neural development and disease mechanism.

BACKGROUND: Seven female individuals with multiple congenital anomalies, developmental delay and/or intellectual disability have been found to have a genetic variant of uncertain significance in the mediator complex subunit 12 gene (MED12 c.3412C>T, p.Arg1138Trp). The functional consequence of this genetic variant in disease is undetermined, and insight into disease mechanism is required. METHODS: We identified a de novo MED12 p.Arg1138Trp variant in a female patient and compared disease phenotypes with six female individuals identified in the literature. To investigate affected biological pathways, we derived two induced pluripotent stem cell (iPSC) lines from the patient: one expressing wildtype MED12 and the other expressing the MED12 p.Arg1138Trp variant. We performed neural disease modelling, transcriptomics and protein analysis, comparing healthy and variant cells. RESULTS: When comparing the two cell lines, we identified altered gene expression in neural cells expressing the variant, including genes regulating RNA polymerase II activity, transcription, pre-mRNA processing, and neural development. We also noted a decrease in MED12L expression. Pathway analysis indicated temporal delays in axon development, forebrain differentiation, and neural cell specification with significant upregulation of pre-ribosome complex gene pathways. CONCLUSION: In a human neural model, expression of MED12 p.Arg1138Trp altered neural cell development and dysregulated the pre-ribosome complex providing functional evidence of disease aetiology and mechanism in MED12-related disorders.

Humans↗

Kalign, Kalignvu and Mumsa: web servers for multiple sequence alignment.

Obtaining high quality multiple alignments is crucial for a range of sequence analysis tasks. A common strategy is to align the sequences several times, varying the program or parameters until the best alignment according to manual inspection by human experts is found. Ideally, this should be assisted by an automatic assessment of the alignment quality. Our web-site http://msa.cgb.ki.se allows users to perform all these steps: Kalign to align sequences, Kalignvu to view and verify the resulting alignments and Mumsa to assess the quality. Due to the computational efficiency of Kalign we can allow users to submit hundreds of sequences to be aligned and still guarantee fast response times. All servers are freely accessible and the underlying software can be freely downloaded for local use.

Internet↗

Pfam: clans, web tools and services.

Pfam is a database of protein families that currently contains 7973 entries (release 18.0). A recent development in Pfam has enabled the grouping of related families into clans. Pfam clans are described in detail, together with the new associated web pages. Improvements to the range of Pfam web tools and the first set of Pfam web services that allow programmatic access to the database and associated tools are also presented. Pfam is available on the web in the UK (http://www.sanger.ac.uk/Software/Pfam/), the USA (http://pfam.wustl.edu/), France (http://pfam.jouy.inra.fr/) and Sweden (http://pfam.cgb.ki.se/).

Computer Graphics↗

Automatic assessment of alignment quality.

Multiple sequence alignments play a central role in the annotation of novel genomes. Given the biological and computational complexity of this task, the automatic generation of high-quality alignments remains challenging. Since multiple alignments are usually employed at the very start of data analysis pipelines, it is crucial to ensure high alignment quality. We describe a simple, yet elegant, solution to assess the biological accuracy of alignments automatically. Our approach is based on the comparison of several alignments of the same sequences. We introduce two functions to compare alignments: the average overlap score and the multiple overlap score. The former identifies difficult alignment cases by expressing the similarity among several alignments, while the latter estimates the biological correctness of individual alignments. We implemented both functions in the MUMSA program and demonstrate the overall robustness and accuracy of both functions on three large benchmark sets.

Algorithms↗

Kalign--an accurate and fast multiple sequence alignment algorithm.

BACKGROUND: The alignment of multiple protein sequences is a fundamental step in the analysis of biological data. It has traditionally been applied to analyzing protein families for conserved motifs, phylogeny, structural properties, and to improve sensitivity in homology searching. The availability of complete genome sequences has increased the demands on multiple sequence alignment (MSA) programs. Current MSA methods suffer from being either too inaccurate or too computationally expensive to be applied effectively in large-scale comparative genomics. RESULTS: We developed Kalign, a method employing the Wu-Manber string-matching algorithm, to improve both the accuracy and speed of multiple sequence alignment. We compared the speed and accuracy of Kalign to other popular methods using Balibase, Prefab, and a new large test set. Kalign was as accurate as the best other methods on small alignments, but significantly more accurate when aligning large and distantly related sets of sequences. In our comparisons, Kalign was about 10 times faster than ClustalW and, depending on the alignment size, up to 50 times faster than popular iterative methods. CONCLUSION: Kalign is a fast and robust alignment method. It is especially well suited for the increasingly important task of aligning large numbers of sequences.

Algorithms↗

Quality assessment of multiple alignment programs.

A renewed interest in the multiple sequence alignment problem has given rise to several new algorithms. In contrast to traditional progressive methods, computationally expensive score optimization strategies are now predominantly employed. We systematically tested four methods (Poa, Dialign, T-Coffee and ClustalW) for the speed and quality of their alignments. As test sequences we used structurally derived alignments from BAliBASE and synthetic alignments generated by Rose. The tests included alignments of variable numbers of domains embedded in random spacer sequences. Overall, Dialign was the most accurate in cases with low sequence identity, while T-Coffee won in cases with high sequence identity. The fast Poa algorithm was almost as accurate, while ClustalW could compete only in strictly global cases with high sequence similarity.

Evolution, Molecular↗