Turner??s Syndrome. Presentation of Four Cases

Décourt, Luiz V.; Gebara, A. J.; Lima, M. Caleiro; Deláscio, D; Chiorboli, E; Fernandes, Joice Maria Pacheco Antonio

doi:10.1097/00006254-195508000-00034

Cited by 2 publications

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“…For nearly a century, progressive discovery of the number and molecular structure of chromosomes and their information content have proven to be useful in the clinical domain [ 1 , 2 ]. With the sequencing of the human genome, we have gained a reference for base pair resolution comparisons that have provided unprecedented insights in molecular and cellular biology.…”

Section: Introductionmentioning

confidence: 99%

Spaced Seed Data Structures forDe NovoAssembly

Birol

Chu

Mohamadi

et al. 2015

International Journal of Genomics

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De novo assembly of the genome of a species is essential in the absence of a reference genome sequence. Many scalable assembly algorithms use the de Bruijn graph (DBG) paradigm to reconstruct genomes, where a table of subsequences of a certain length is derived from the reads, and their overlaps are analyzed to assemble sequences. Despite longer subsequences unlocking longer genomic features for assembly, associated increase in compute resources limits the practicability of DBG over other assembly archetypes already designed for longer reads. Here, we revisit the DBG paradigm to adapt it to the changing sequencing technology landscape and introduce three data structure designs for spaced seeds in the form of paired subsequences. These data structures address memory and run time constraints imposed by longer reads. We observe that when a fixed distance separates seed pairs, it provides increased sequence specificity with increased gap length. Further, we note that Bloom filters would be suitable to implicitly store spaced seeds and be tolerant to sequencing errors. Building on this concept, we describe a data structure for tracking the frequencies of observed spaced seeds. These data structure designs will have applications in genome, transcriptome and metagenome assemblies, and read error correction.

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Section: Introductionmentioning

confidence: 99%

Spaced Seed Data Structures forDe NovoAssembly

Birol

Chu

Mohamadi

et al. 2015

International Journal of Genomics

View full text Add to dashboard Cite

show abstract

“…For nearly a century, progressive discovery of the number and molecular structure of chromosomes and their information content have proven to be very useful in the clinical domain [1,2]. With the human genome sequenced, we have gained a reference for base pair resolution comparisons that have provided unprecedented insights in molecular and cellular biology.…”

Section: Introductionmentioning

confidence: 99%

Spaced seed data structures

Birol

Mohamadi

Raymond

et al. 2014

2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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This past decade, genome sciences have benefitted from rapid advances in DNA sequencing technologies, and development of efficient algorithms for processing short nucleotide sequences played a key role in enabling their uptake in the field. In particular, reassembly of human genomes (de novo or reference-guided) from short DNA sequence reads had a substantial impact on health research. De novo assembly of a genome is essential in the absence of a reference genome sequence of a species. It is also gaining traction even when one is available, due to the utility of the method to resolve ambiguous or rearranged genomic regions with high specificity. With commercial high-throughput sequencing technologies increasing their throughput and their read lengths, the de Bruijn graph (DBG) paradigm used by many assembly algorithms needs to be revisited. DBG uses a table of k-mers, sequences of length k base pairs derived from the reads, and their k-1 base pair overlaps to assemble sequences. Despite longer k-mers unlocking longer genomic features for assembly, associated increases in memory usage and other compute resources are tradeoffs that limit the practicability of DBG over other assembly archetypes already designed for longer reads. Here, we introduce three data structure designs for paired k-mers, or spaced seeds, each addressing memory and run time constraints imposed by longer reads. In spaced seeds, a fixed distance separates k-mer pairs, providing increased sequence specificity with increased distance, while keeping memory usage low. Further, we describe a data structure based on Bloom filters that would be suitable to implicitly store spaced seeds, and would be tolerant to sequencing errors. Building on the spaced seeds Bloom filter, we describe a data structure for tracking the frequencies of observed spaced seeds. We expect the data structure designs we introduce in this study to have broad applications in genomics research, with niche applications in genome, transcriptome and metagenome assemblies, and in read error correction.

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Turner??s Syndrome. Presentation of Four Cases

Cited by 2 publications

References 0 publications

Spaced Seed Data Structures forDe NovoAssembly

Spaced Seed Data Structures forDe NovoAssembly

Spaced seed data structures

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