Challenges in identifying large germline structural variants for clinical use by long read sequencing

Bizjan, Barbara Jenko; Κάτσιλα, Θεοδώρα; Tesovnik, Tine; Šket, Robert; Debeljak, Maruša; Matsoukas, Minos‐Timotheos; Kovač, Jernej

doi:10.1016/j.csbj.2019.11.008

Cited by 20 publications

(11 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CNV analysis is limited to the determination of RHD zygosity and to distinguish between the alleles for the C/c antigen. The automation of hybrid alleles (e.g., RHCE*ce-D 4 , 5 , 6 , 7 -ce and GPB 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 -A (47–118)) prediction, is highly dependent on statistical normalization and batch- corrections methodologies which are used in the read alignment tools. To eliminate coverage biases in homologous recombination regions, short-read base alignment methods are still struggling to obtain unique mapped reads.…”

Section: Discussionmentioning

confidence: 99%

“…To eliminate coverage biases in homologous recombination regions, short-read base alignment methods are still struggling to obtain unique mapped reads. 42 , 43 Recent developments in the detection of structural/copy number variations are elucidating the challenges around complex genomics regions, 44 , 45 , 46 but approaches like alternative mapping locus and a combination of short and long-read sequencing data 43 , 47 will help to improve hybrid allele prediction. The RBCeq predicts novel variants with the potential of clinical relevance by applying the in-silico tools.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

RBCeq: A robust and scalable algorithm for accurate genetic blood typing

Jadhao¹,

Davison²,

Roulis³

et al. 2022

eBioMedicine

View full text Add to dashboard Cite

Background While blood transfusion is an essential cornerstone of hematological care, patients requiring repetitive transfusion remain at persistent risk of alloimmunization due to the diversity of human blood group polymorphisms. Despite the promise, user friendly methods to accurately identify blood types from next-generation sequencing data are currently lacking. To address this unmet need, we have developed RBCeq, a novel genetic blood typing algorithm to accurately identify 36 blood group systems.Methods RBCeq can predict complex blood groups such as RH, and ABO that require identification of small indels and copy number variants. RBCeq also reports clinically significant, rare, and novel variants with potential clinical relevance that may lead to the identification of novel blood group alleles.Findings The RBCeq algorithm demonstrated 99¢07% concordance when validated on 402 samples which included 29 antigens with serology and 9 antigens with SNP-array validation in 14 blood group systems and 59 antigens validation on manual predicted phenotype from variant call files. We have also developed a user-friendly web server that generates detailed blood typing reports with advanced visualization (https://www.rbceq.org/). Interpretation RBCeq will assist blood banks and immunohematology laboratories by overcoming existing methodological limitations like scalability, reproducibility, and accuracy when genotyping and phenotyping in multi-ethnic populations. This Amazon Web Services (AWS) cloud based platform has the potential to reduce pre-transfusion testing time and to increase sample processing throughput, ultimately improving quality of patient care.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

RBCeq: A robust and scalable algorithm for accurate genetic blood typing

Jadhao¹,

Davison²,

Roulis³

et al. 2022

eBioMedicine

View full text Add to dashboard Cite

show abstract

“…To automate the hybrid allele detection process, far clearer descriptions of the methods are required to facilitate robust predictions. Even so, as the number of relevant studies continues to increase, these complex phenotypes may be better clarified (39-41).…”

Section: Discussionmentioning

confidence: 99%

RBCeq: An Integrated Bioinformatics Algorithm Designed to Improve Blood Type Compatibility Testing

Jadhao

Davison

Roulis

et al. 2021

Preprint

View full text Add to dashboard Cite

While blood transfusion is an essential cornerstone of hematological care, patients that require repetitive transfusion remain at persistent risk of alloimmunization due to the diversity of human blood group polymorphisms. Next-generation sequencing (NGS) is an effective means of identifying genotypic and phenotypic variations among the blood groups, while the accurate interpretation of such NGS data is currently hampered by a lack of accessibility to bioinformatics support. To address this unmet need, we have developed the RBCeq (https://www.rbceq.org/) platform, which consists of a novel bioinformatics algorithm coupled with a user-friendly web server capable of comprehensively delineating different blood group variants from genomics data with advanced visualization of results. The software profiles genomic data for 36 blood group systems, including two transcription factors and can identify small genetic alterations, including small indels and copy number variants. The RBCeq algorithm was validated on 403 samples which include 58 complex serology cases from Australian Red Cross LifeBlood, 100 samples from The MedSeq Project (phs000958) and a further 245 from Indigenous Australian participants. The final blood typing data from RBCeq was 99.83% concordant for 403 samples (85 different antigens in 21 blood group systems) with that listed from the International Society for Blood Transfusion database.

show abstract

“…Several articles have been published that reviewed long-read sequencing technologies [1] , [36] , [37] , [38] , [39] , [40] , [41] . However, the technologies used for long-read sequencing are rapidly changing.…”

Section: Introductionmentioning

confidence: 99%