scholarly journals Prefix-Free Parsing for Building Big BWTs

2018 ◽  
Author(s):  
Christina Boucher ◽  
Travis Gagie ◽  
Alan Kuhnle ◽  
Ben Langmead ◽  
Giovanni Manzini ◽  
...  
Keyword(s):  
Human Chromosome ◽  
Human Genome ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Total Size ◽  
Genomic Databases ◽  
Internal Memory ◽  
Sequencing Technologies ◽  
Burrows Wheeler Transform ◽  
Human Chromosome 19

AbstractHigh-throughput sequencing technologies have led to explosive growth of genomic databases; one of which will soon reach hundreds of terabytes. For many applications we want to build and store indexes of these databases but constructing such indexes is a challenge. Fortunately, many of these genomic databases are highly-repetitive—a characteristic that can be exploited to ease the computation of the Burrows-Wheeler Transform (BWT), which underlies many popular indexes. In this paper, we introduce a preprocessing algorithm, referred to as prefix-free parsing, that takes a text T as input, and in one-pass generates a dictionary D and a parse P of T with the property that the BWT of T can be constructed from D and P using workspace proportional to their total size and O(|T|)-time. Our experiments show that D and P are significantly smaller than T in practice, and thus, can fit in a reasonable internal memory even when T is very large. In particular, we show that with prefix-free parsing we can build an 131-megabyte run-length compressed FM-index (restricted to support only counting and not locating) for 1000 copies of human chromosome 19 in 2 hours using 21 gigabytes of memory suggesting that we can build a 6.73 gigabyte index for 1000 complete human-genome haplotypes in approximately 102 hours using about 1 terabyte of memory

scholarly journals Long Noncoding RNA Identification: Comparing Machine Learning Based Tools for Long Noncoding Transcripts Discrimination

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Siyu Han ◽  
Yanchun Liang ◽  
Ying Li ◽  
Wei Du
Keyword(s):  
Machine Learning ◽  
Human Genome ◽  
High Throughput ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
High Throughput Sequencing ◽  
Great Influence ◽  
Suitable Method ◽  
Cellular Process ◽  
Sequencing Technologies

Long noncoding RNA (lncRNA) is a kind of noncoding RNA with length more than 200 nucleotides, which aroused interest of people in recent years. Lots of studies have confirmed that human genome contains many thousands of lncRNAs which exert great influence over some critical regulators of cellular process. With the advent of high-throughput sequencing technologies, a great quantity of sequences is waiting for exploitation. Thus, many programs are developed to distinguish differences between coding and long noncoding transcripts. Different programs are generally designed to be utilised under different circumstances and it is sensible and practical to select an appropriate method according to a certain situation. In this review, several popular methods and their advantages, disadvantages, and application scopes are summarised to assist people in employing a suitable method and obtaining a more reliable result.

scholarly journals Assessing genotyping errors in mammalian museum study skins using high-throughput genotyping-by-sequencing

2021 ◽  
Author(s):  
Stella C. Yuan ◽  
Eric Malekos ◽  
Melissa T. R. Hawkins
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Massively Parallel Sequencing ◽  
Massively Parallel ◽  
Museum Specimens ◽  
Museum Specimen ◽  
Genotyping Errors ◽  
Allelic Dropout ◽  
Parallel Sequencing ◽  
Sequencing Technologies

AbstractThe use of museum specimens held in natural history repositories for population and conservation genetic research is increasing in tandem with the use of massively parallel sequencing technologies. Short Tandem Repeats (STRs), or microsatellite loci, are commonly used genetic markers in wildlife and population genetic studies. However, they traditionally suffered from a host of issues including length homoplasy, high costs, low throughput, and difficulties in reproducibility across laboratories. Massively parallel sequencing technologies can address these problems, but the incorporation of museum specimen derived DNA suffers from significant fragmentation and exogenous DNA contamination. Combatting these issues requires extra measures of stringency in the lab and during data analysis, yet there have not been any high-throughput sequencing studies evaluating microsatellite allelic dropout from museum specimen extracted DNA. In this study, we evaluate genotyping errors derived from mammalian museum skin DNA extracts for previously characterized microsatellites across PCR replicates utilizing high-throughput sequencing. We found it useful to classify samples based on DNA concentration, which determined the rate by which genotypes were accurately recovered. Longer microsatellites performed worse in all museum specimens. Allelic dropout rates across loci were dependent on sample quantity, with high concentration museum specimens performing as well and recovering quality metrics nearly as high as the frozen tissue sample. Based on our results, we provide a set of best practices for quality assurance and incorporation of reliable genotypes from museum specimens.

scholarly journals Detection of novel allelic variations in soybean mutant population using Tilling by Sequencing

2019 ◽  
Author(s):  
Reneth Millas ◽  
Mary Espina ◽  
CM Sabbir Ahmed ◽  
Angelina Bernardini ◽  
Ekundayo Adeleke ◽  
...  
Keyword(s):  
Fatty Acid ◽  
High Throughput ◽  
Reverse Genetics ◽  
High Throughput Sequencing ◽  
Fatty Acid Biosynthesis ◽  
Induced Mutations ◽  
Mutant Population ◽  
Sequencing Technologies ◽  
Allelic Variations ◽  
Tilling By Sequencing

ABSTRACTOne of the most important tools in genetic improvement is mutagenesis, which is a useful tool to induce genetic and phenotypic variation for trait improvement and discovery of novel genes. JTN-5203 (MG V) mutant population was generated using an induced ethyl methane sulfonate (EMS) mutagenesis and was used for detection of induced mutations in FAD2-1A and FAD2-1B genes using reverse genetics approach. Optimum concentration of EMS was used to treat 15,000 bulk JTN-5203 seeds producing 1,820 M2 population. DNA was extracted, normalized, and pooled from these individuals. Specific primers were designed from FAD2-1A and FAD2-1B genes that are involved in the fatty acid biosynthesis pathway for further analysis using next-generation sequencing. High throughput mutation discovery through TILLING-by-Sequencing approach was used to detect novel allelic variations in this population. Several mutations and allelic variations with high impacts were detected for FAD2-1A and FAD2-1B. This includes GC to AT transition mutations in FAD2-1A (20%) and FAD2-1B (69%). Mutation density for this population is estimated to be about 1/136kb. Through mutagenesis and high-throughput sequencing technologies, novel alleles underlying the mutations observed in mutants with reduced polyunsaturated fatty acids will be identified, and these mutants can be further used in breeding soybean lines with improved fatty acid profile, thereby developing heart-healthy-soybeans.

scholarly journals Utilizing the VirIdAl Pipeline to Search for Viruses in the Metagenomic Data of Bat Samples

Viruses ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2006
Author(s):  
Anna Y Budkina ◽  
Elena V Korneenko ◽  
Ivan A Kotov ◽  
Daniil A Kiselev ◽  
Ilya V Artyushin ◽  
...  
Keyword(s):  
Large Scale ◽  
High Throughput Sequencing ◽  
Metagenomic Data ◽  
Sequencing Data ◽  
Viral Pathogens ◽  
Genomic Databases ◽  
Bioinformatic Pipeline ◽  
Viral Genomes ◽  
Sequencing Technologies ◽  
Viral Screening

According to various estimates, only a small percentage of existing viruses have been discovered, naturally much less being represented in the genomic databases. High-throughput sequencing technologies develop rapidly, empowering large-scale screening of various biological samples for the presence of pathogen-associated nucleotide sequences, but many organisms are yet to be attributed specific loci for identification. This problem particularly impedes viral screening, due to vast heterogeneity in viral genomes. In this paper, we present a new bioinformatic pipeline, VirIdAl, for detecting and identifying viral pathogens in sequencing data. We also demonstrate the utility of the new software by applying it to viral screening of the feces of bats collected in the Moscow region, which revealed a significant variety of viruses associated with bats, insects, plants, and protozoa. The presence of alpha and beta coronavirus reads, including the MERS-like bat virus, deserves a special mention, as it once again indicates that bats are indeed reservoirs for many viral pathogens. In addition, it was shown that alignment-based methods were unable to identify the taxon for a large proportion of reads, and we additionally applied other approaches, showing that they can further reveal the presence of viral agents in sequencing data. However, the incompleteness of viral databases remains a significant problem in the studies of viral diversity, and therefore necessitates the use of combined approaches, including those based on machine learning methods.

scholarly journals Adenosine-to-inosine RNA editing may be implicated in human pathogenesis

2019 ◽  
pp. 22-25
Author(s):  
AA Kliuchnikova ◽  
SA Moshkovskii
Keyword(s):  
Immune Responses ◽  
Rna Editing ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Common Mechanism ◽  
Adenosine Deaminases ◽  
Human Transcriptome ◽  
Sequencing Technologies

Adenosine-to-inosine (A-to-I) RNA editing is a common mechanism of post-transcriptional modification in many metazoans including vertebrates; the process is catalyzed by adenosine deaminases acting on RNA (ADARs). Using high-throughput sequencing technologies resulted in finding thousands of RNA editing sites throughout the human transcriptome however, their functions are still poorly understood. The aim of this brief review is to draw attention of clinicians and biomedical researchers to ADAR-mediated RNA editing phenomenon and its possible implication in development of neuropathologies, antiviral immune responses and cancer.

Minirmd: accurate and fast duplicate removal tool for short reads via multiple minimizers

2020 ◽  
Author(s):  
Yuansheng Liu ◽  
Xiaocai Zhang ◽  
Quan Zou ◽  
Xiangxiang Zeng
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
De Novo ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Complementary Strand ◽  
Short Reads ◽  
Sequencing Technologies ◽  
Computational Resources

Abstract Summary Removing duplicate and near-duplicate reads, generated by high-throughput sequencing technologies, is able to reduce computational resources in downstream applications. Here we develop minirmd, a de novo tool to remove duplicate reads via multiple rounds of clustering using different length of minimizer. Experiments demonstrate that minirmd removes more near-duplicate reads than existing clustering approaches and is faster than existing multi-core tools. To the best of our knowledge, minirmd is the first tool to remove near-duplicates on reverse-complementary strand. Availability and implementation https://github.com/yuansliu/minirmd. Supplementary information Supplementary data are available at Bioinformatics online.

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