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Related Concept Videos

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Genomic Imprinting and Inheritance02:30

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Diploid organisms inherit genetic material through chromosomes from both parents. Copies of the same gene are known as alleles. In most cases, both alleles are simultaneously expressed and allow various cellular processes to function optimally. If one of the alleles is missing or mutated, the expression of the other allele can compensate; however, this is not true for all genes.
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Genome Size and the Evolution of New Genes03:21

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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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Cis-regulatory Sequences02:02

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Trial and Error and Algorithm01:12

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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes02:16

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The present-day mitochondrial and chloroplast genomes have retained some of the characteristics of their ancestral prokaryotes and also have acquired new attributes during their evolution within eukaryotic cells. Like prokaryotic genomes, mitochondrial and chloroplast genomes neither bind with histone-like proteins nor show complex packaging into chromosome-like structures, as observed in eukaryotes. Unlike mitotic cell divisions observed in eukaryotic cells, mitochondria and chloroplasts...
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Updated: Jan 30, 2026

Ultra-long Read Sequencing for Whole Genomic DNA Analysis
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Ultra-long Read Sequencing for Whole Genomic DNA Analysis

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pblat: a multithread blat algorithm speeding up aligning sequences to genomes.

Meng Wang1, Lei Kong2

  • 1Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, 100871, People's Republic of China.

BMC Bioinformatics
|January 17, 2019
PubMed
Summary
This summary is machine-generated.

Parallel blat (pblat) significantly speeds up whole genome and transcriptome sequence mapping. This parallelized tool offers high-speed, high-precision mapping for large-scale genomic projects without compromising accuracy.

Keywords:
Cluster computingGenome annotationParallel computingSequence alignment

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Area of Science:

  • Bioinformatics
  • Computational Biology

Background:

  • The blat tool is essential for long sequence and gapped mapping but is single-threaded, limiting its use in large-scale projects.
  • Its single-threaded nature results in lengthy processing times, hindering iterative analysis and high-throughput sequencing.

Purpose of the Study:

  • To develop a parallelized version of the blat algorithm to accelerate sequence mapping.
  • To enhance the efficiency of mapping large DNA/RNA sequences to reference genomes.

Main Methods:

  • Introduced pblat, a parallelized blat algorithm with multithread and cluster computing support.
  • Implemented pblat-cluster for distributed computing environments using MPI.

Main Results:

  • pblat significantly reduces run time by leveraging multicore processors.
  • The pblat algorithm produces identical results to the original blat tool.
  • pblat is easily installable on Linux and Mac OS systems.

Conclusions:

  • pblat and pblat-cluster provide a free, open-source solution for high-throughput sequence mapping.
  • The tools facilitate rapid and precise mapping of large genomic and transcript sequences.
  • Ease of installation and use makes pblat suitable for large-scale bioinformatics applications.