Address of the bookmark: https://github.com/imgag/ClinCNV

Additional Information:

If you have any questions about scientific aspects of this position, please contact Prof. Lars Bertram, head of LIGA (lars.bertram@uni-luebeck.de).

Please contact Ms. Anna Wolbert for further questions about administrative details (recruiting@uksh.de).

Weitere Informationen erhalten Sie auch unter www.uksh.de/karriere.

Wir freuen uns auf Ihre Bewerbung bis zum 15.03.2020 unter Angabe unserer Ausschreibungsnummer 20190570.119.CL.

More at https://genohub.com/ngs-instrument-guide/

Address of the bookmark: https://genohub.com/ngs-instrument-guide/

A recent study published in Genome Biology (https://link.springer.com/article/10.1186/s13059-026-04280-y) addressed this challenge by introducing dicast (https://github.com/burgshrimps/dicast), a machine-learning approach for detecting structural variants in short-read sequencing data. Structural variants, such as large deletions, insertions, duplications, and inversions, can have significant biological and clinical implications, yet they are challenging to identify with short-read technologies. Because different detection methods frequently yield divergent results, researchers commonly employ consensus calling, considering a variant valid only if multiple tools detect it. While this approach reduces false positives, it relies on the potentially flawed assumption that the majority is always correct.

The researchers explored the impact of evaluating the supporting evidence for each variant, rather than simply tallying the number of algorithms that identified it. To establish a ground truth, they analyzed nine genomes using multiple sequencing technologies and 15 detection methods, initially identifying approximately 35 million potential variants. Through extensive filtering, evidence integration, and manual review of over 11,500 variants, they developed a robust benchmark comprising more than 236,000 structural variants. The findings underscored the complexity of the problem: short-read methods detected fewer than half of deletions and less than 10 percent of insertions, with performance declining markedly in repetitive genomic regions. In contrast, long-read technologies demonstrated superior detection capabilities. However, replacing the substantial volume of existing short-read data in clinical and research settings is not immediately feasible. Consequently, the researchers questioned whether short-read data might harbor more information than conventional analytical pipelines currently extract.

This line of inquiry led to the development of dicast. Rather than merely confirming agreement among multiple tools, dicast identifies patterns in sequencing data, including split and clipped reads, discordant read pairs, alignment characteristics, and the surrounding genomic context. An XGBoost machine-learning model evaluates which combinations of these signals are indicative of genuine structural variants. Thus, the approach shifts from tallying algorithmic consensus to interpreting the underlying evidence.

The researchers subsequently conducted a targeted evaluation by examining structural variants detected by only a single short-read tool, which are typically missed by consensus-based approaches. dicast successfully recovered approximately 81% of these single-caller deletions, insertions, and duplications. The signals for these variants were present in the data, but conventional filtering methods failed to integrate them effectively.

The utility of dicast was further demonstrated in cohorts with rare diseases, including congenital limb malformations, atrial fibrillation, and neuromuscular disorders. In one instance, dicast achieved a deletion recall rate of approximately 0.96, compared to 0.74 using consensus calling. The median number of variants requiring manual review was 29 per sample. Among 31 experimentally validated variants that standard filters would have missed, dicast identified 12, whereas consensus calling detected only one.

Overall, dicast identified approximately 20 percent more potential disease-causing deletions than consensus-based methods. While a 20 percent increase may appear modest, in clinical genomics such improvements can have significant practical implications. Missing a deletion may leave a case unresolved, whereas detecting a structural variant can provide critical diagnostic insights.

The study does not claim that machine learning has rendered short-read sequencing superior to long-read approaches. Instead, the results underscore the effectiveness of long-read sequencing for structural variant detection. However, dicast highlights a more nuanced perspective: substantial biological information may still be recoverable from the extensive short-read datasets already available.

The principal lesson extends beyond the detection of structural variants. For many years, bioinformatics pipelines have relied on threshold-based criteria, such as minimum coverage, quality scores, or support from multiple tools. While these rules are useful, biological phenomena do not always conform to rigid checklists; multiple weak signals, when considered collectively, can provide compelling evidence.

This perspective prompts consideration of the solitary variant: one algorithm identifies it, while several others do not. Traditional consensus techniques might have dismissed it, yet machine learning approaches evaluate the available evidence to determine whether the variant is plausible.

Occasionally, the most significant variant within a genome is the one that is almost universally overlooked.

Read more at https://link.springer.com/article/10.1186/s13059-026-04280-y

Address of the bookmark: https://github.com/broadinstitute/VariantBam

More at https://kevlar.readthedocs.io/en/latest/

https://www.cell.com/iscience/pdf/S2589-0042(19)30259-7.pdf

Address of the bookmark: https://github.com/kevlar-dev/kevlar

• Assembly of nanopore reads using Canu.
• Polish canu contigs using racon (optional).
• Map a paired-end Illumina dataset onto the contigs obtained in the previous steps using BWA mem.
• Perform correction of contigs using pilon and the Illumina dataset.

Address of the bookmark: https://github.com/nanoporetech/ont-assembly-polish

Yau Group develops statistical and computational methods for the analysis of genomic datasets with a particular interest in cancer sequencing applications and the use of Bayesian Statistics.

Yau Group are currently have projects in somatic mutation analysis of heterogeneous cancers, data fusion or integration techniques and single cell genomics.

More @ http://www.well.ox.ac.uk/~cyau/index.html

The Department is at the forefront of developments in machine learning, data science, and computational methods for understanding complex biological systems, integrating these approaches into both its research and teaching. This role offers significant scope to shape the future of bioinformatics and quantitative biology at Oxford and beyond.

You will be expected to develop and lead a world-class research programme in bioinformatics, helping to strengthen Oxford's position in areas such as statistical genomics, computational biology, machine learning for biological data, engineering biology, systems biology, and biomedical data science. We value the breadth of approaches that contribute to modern bioinformatics. Whatever your particular focus, you will understand how quantitative and computational methods can be applied to important biological and biomedical questions, and how collaboration across disciplines can generate new insights and opportunities. Alongside your own research, you will support the development of doctoral students, postdoctoral researchers, and early-career academics. You will contribute to our teaching at undergraduate and postgraduate level, delivering lectures, supervising student projects, and supporting graduate research students.

We are looking for someone who combines scholarly distinction with a collaborative and ambitious approach to research. You will be a researcher with an international reputation in bioinformatics, with a strong record of producing influential research at the interface of statistics, computational biology, genomics, and data science, and of securing research funding. Your work should advance the field, whether through methodological innovation, biological discovery, or the development of new approaches to understanding complex biological and biomedical data. You will bring experience of shaping research strategy, building productive collaborations, and exercising academic leadership. Above all, we wish to appoint someone who shares our values of intellectual curiosity, academic excellence, scholarly integrity, and a commitment to fostering a positive, supportive, and inclusive environment for all.

If you would like to discuss this post, please contact the Head of the Department of Statistics, Professor Frank Windmeijer, frank.windmeijer@stats.ox.ac.uk, or for information about University College please contact the Senior Tutor, Dr Andrew Bell, andrew.bell@univ.ox.ac.uk. All enquiries will be treated in strict confidence and will not form part of the selection decision.

The closing date for applications is 12:00 noon UK time on Monday 21 September 2026. Interviews are expected to be held in November 2026.

Apply here https://my.corehr.com/pls/uoxrecruit/erq_jobspec_version_4.display_form

more at https://ccb.jhu.edu/software/hisat2/index.shtml

Address of the bookmark: https://github.com/infphilo/hisat2