In Silico identification of a common mobile element insertion in exon 4 of RP1

Mobile element insertions (MEIs) typically exceed the read lengths of short-read sequencing technologies and are therefore frequently missed. Recently, a founder Alu insertion in exon 4 of RP1 has been detected in Japanese patients with macular dystrophy by PCR and gel electrophoresis. We aimed to develop a grep search program for the detection of the Alu insertion in exon 4 of RP1 using unprocessed short reads. Among 494 unrelated Korean patients with inherited eye diseases, 273 patients with specific retinal phenotypes who were previously genotyped by targeted panel or whole exome sequencing were selected. Five probands had a single heterozygous truncating RP1 variant, and one of their unaffected parents also carry this variant. To find a hidden genetic variant, whole genome sequencing was performed in two patients, and it revealed AluY c.4052_4053ins328/p.(Tyr1352Alafs*9) insertion in RP1 exon 4. This AluY insertion was additionally identified in other 3 families, which was confirmed by PCR and gel electrophoresis. We developed simplified grep search program to detect this AluY insertion in RP1 exon 4. The simple grep search revealed a median variant allele frequency of 0.282 (interquartile range, 0.232–0.383), with no false-positive results using 120 control samples. The MEI in RP1 exon 4 was a common founder mutation in Korean, occurring in 1.8% of our cohort. The RP1-Alu grep program efficiently detected the AluY insertion, without the preprocessing of raw data or complex installation processes.

Early-onset breast cancer in a woman with a germline mobile element insertion resulting in BRCA2 disruption: a case report

Mobile element insertions (MEIs) contribute to genomic diversity, but they can be responsible for human disease in some cases. Initial clinical testing (BRCA1, BRCA2 and PALB2) in a 40-year-old female with unilateral breast cancer did not detect any pathogenic variants. Subsequent reanalysis for MEIs detected a novel likely pathogenic insertion of the retrotransposon element (RE) c.7894_7895insSVA in BRCA2. This case highlights the importance of bioinformatic pipeline optimization for the detection of MEIs in genes associated with hereditary cancer, as early detection can significantly impact clinical management.

rMETL: sensitive mobile element insertion detection with long read realignment

Summary Mobile element insertion (MEI) is a major category of structure variations (SVs). The rapid development of long read sequencing technologies provides the opportunity to detect MEIs sensitively. However, the signals of MEI implied by noisy long reads are highly complex due to the repetitiveness of mobile elements as well as the high sequencing error rates. Herein, we propose the Realignment-based Mobile Element insertion detection Tool for Long read (rMETL). Benchmarking results of simulated and real datasets demonstrate that rMETL enables to handle the complex signals to discover MEIs sensitively. It is suited to produce high-quality MEI callsets in many genomics studies. Availability and implementation rMETL is available from https://github.com/hitbc/rMETL. Supplementary information Supplementary data are available at Bioinformatics online.

Mobile genetic element insertions drive antibiotic resistance across pathogens

Mobile genetic elements contribute to bacterial adaptation and evolution; however, detecting these elements in a high-throughput and unbiased manner remains challenging. Here, we demonstrate ade novoapproach to identify mobile elements from short-read sequencing data. The method identifies the precise site of mobile element insertion and infers the identity of the inserted sequence. This is an improvement over previous methods that either rely on curated databases of known mobile elements or rely on ‘split-read’ alignments that assume the inserted element exists within the reference genome. We apply our approach to 12,419 sequenced isolates of nine prevalent bacterial pathogens, and we identify hundreds of known and novel mobile genetic elements, including many candidate insertion sequences. We find that the mobile element repertoire and insertion rate vary considerably across species, and that many of the identified mobile elements are biased toward certain target sequences, several of them being highly specific. Mobile element insertion hotspots often cluster near genes involved in mechanisms of antibiotic resistance, and such insertions are associated with antibiotic resistance in laboratory experiments and clinical isolates. Finally, we demonstrate that mutagenesis caused by these mobile elements contributes to antibiotic resistance in a genome-wide association study of mobile element insertions in pathogenicEscherichia coli. In summary, by applying ade novoapproach to precisely identify mobile genetic elements and their insertion sites, we thoroughly characterize the mobile element repertoire and insertion spectrum of nine pathogenic bacterial species and find that mobile element insertions play a significant role in the evolution of clinically relevant phenotypes, such as antibiotic resistance.

rMETL: sensitive mobile element insertion detection with long read realignment

SummaryMobile element insertion (MEI) is a major category of structure variations (SVs). The rapid development of long read sequencing provides the opportunity to sensitively discover MEIs. However, the signals of MEIs implied by noisy long reads are highly complex, due to the repetitiveness of mobile elements as well as the serious sequencing errors. Herein, we propose Realignment-based Mobile Element insertion detection Tool for Long read (rMETL). rMETL takes advantage of its novel chimeric read re-alignment approach to well handle complex MEI signals. Benchmarking results on simulated and real datasets demonstrated that rMETL has the ability to more sensitivity discover MEIs as well as prevent false positives. It is suited to produce high quality MEI callsets in many genomics studies.Availability and Implementation: rMETL is available from https://github.com/hitbc/rMETL.Contact:[email protected] information: Supplementary data are available at Bioinformatics online.

Toolbox for Mobile-Element Insertion Detection on Cancer Genomes

Mobile elements constitute greater than 45% of the human genome as a result of repeated insertion events during human genome evolution. Although most of mobile elements are fixed within the human population, some elements (including ALU, long interspersed elements (LINE) 1 (L1), and SVA) are still actively duplicating and may result in life-threatening human diseases such as cancer, motivating the need for accurate mobile-element insertion (MEI) detection tools. We developed a software package, TANGRAM, for MEI detection in next-generation sequencing data, currently serving as the primary MEI detection tool in the 1000 Genomes Project. TANGRAM takes advantage of valuable mapping information provided by our own MOSAIK mapper, and until recently required MOSAIK mappings as its input. In this study, we report a new feature that enables TANGRAM to be used on alignments generated by any mainstream short-read mapper, making it accessible for many genomic users. To demonstrate its utility for cancer genome analysis, we have applied TANGRAM to the TCGA (The Cancer Genome Atlas) mutation calling benchmark 4 dataset. TANGRAM is fast, accurate, easy to use, and open source on https://github.com/jiantao/Tangram .

Toolbox for Mobile-Element Insertion Detection on Cancer Genomes

Mobile elements constitute greater than 45% of the human genome as a result of repeated insertion events during human genome evolution. Although most of mobile elements are fixed within the human population, some elements (including ALU, long interspersed elements (LINE) 1 (L1), and SVA) are still actively duplicating and may result in life-threatening human diseases such as cancer, motivating the need for accurate mobile-element insertion (MEI) detection tools. We developed a software package, TANGRAM, for MEI detection in next-generation sequencing data, currently serving as the primary MEI detection tool in the 1000 Genomes Project. TANGRAM takes advantage of valuable mapping information provided by our own MOSAIK mapper, and until recently required MOSAIK mappings as its input. In this study, we report a new feature that enables TANGRAM to be used on alignments generated by any mainstream short-read mapper, making it accessible for many genomic users. To demonstrate its utility for cancer genome analysis, we have applied TANGRAM to the TCGA (The Cancer Genome Atlas) mutation calling benchmark 4 dataset. TANGRAM is fast, accurate, easy to use, and open source on https://github.com/jiantao/Tangram .