TimeLogic

Last updated
TimeLogic
Company type Privately held company
Industry Bioinformatics Hardware and Software
Founded1981
Headquarters Carlsbad, CA, USA
Area served
Worldwide
ProductsDeCypher, Tera-BLAST, DeCypherSW, DeCypherHMM, GeneDetective, PipeWorks
Parent Active Motif, Inc.
Website TimeLogic

TimeLogic was the bioinformatics division of Active Motif, Inc. The company is headquartered in Carlsbad, California. TimeLogic developed FPGA-accelerated tools for biological sequence comparison in the field of high performance bioinformatics and biocomputing.

Contents

History

TimeLogic was founded in 1981 by James W. (Jim) Lindelien and developed one of the first commercial hardware-accelerated tools for bioinformatics, an FPGA-accelerated version of the Smith-Waterman algorithm. TimeLogic's DeCypher systems have expanded to provide accelerated implementations of the ubiquitous bioinformatics algorithms BLAST, Smith-Waterman, and HMMER using field programmable gate array (FPGA) technology.

In 2003, TimeLogic was acquired by Active Motif, [1] a biotechnology reagent company started by Invitrogen co-founder Joseph Fernandez.

In 2008, TimeLogic formed a partnership with Biomatters to integrate Geneious Pro with the accelerated algorithms on DeCypher systems. [2]

In 2011, TimeLogic formed a partnership with Bielefeld University's Center for Biotechnology (CeBiTec) to jointly develop accelerated computational tools. [3]

Selected scientific contributions

Accelerated bioinformatics algorithms have played an important role in high throughput genomics, and DeCypher systems have been widely published as an enabling technology for genomic discovery in over 180 peer-reviewed scientific research articles, including the selected milestones below:

In 1997, the annotation of the first complete sequence of the E. coli K12 genome used DeCypher Smith-Waterman to determine the function of new translated sequences. [4]

In 2002, the rice genome, the first completely sequenced crop, [5] was annotated using DeCypher FrameSearch "to detect and guide the correction of frameshifts caused by indels." [6]

In 2004, a high throughput genomic approach to the study of horizontal gene transfer in plant-parasitic nematodes [7] was conducted using DeCypher Tera-BLAST, Timelogic's implementation of the BLAST algorithm.

In 2007, HMM profiling of metagenomics sequences generated by the Sorcerer II Global Ocean Sampling Expedition (GOS) were performed using DeCypherHMM to discover 1700 new protein families and matches to 6000 sequences previously categorized in scientific literature as ORFans. [8] Dr. Craig Venter credited TimeLogic in his biography, noting that the DeCypher system performed "an order of magnitude or two more than had been achieved before. The final computation took two weeks but would have run for well more than a century on a standard computer." [9]

Also in 2007, a physical map of the soybean pathogen Fusarium virguliforme was developed using exonic fragments identified with DeCypher GeneDetective. [10]

In 2011, a global assessment of the genomic variation in cattle was conducted using DeCypher Tera-BLAST "to accurately detect chromosomal positions of the SNP sites." [11]

Products

See also

Related Research Articles

<span class="mw-page-title-main">Bioinformatics</span> Computational analysis of large, complex sets of biological data

Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The process of analyzing and interpreting data can sometimes be referred to as computational biology, however this distinction between the two terms is often disputed. To some, the term computational biology refers to building and using models of biological systems.

In bioinformatics, sequence analysis is the process of subjecting a DNA, RNA or peptide sequence to any of a wide range of analytical methods to understand its features, function, structure, or evolution. It can be performed on the entire genome, transcriptome or proteome of an organism, and can also involve only selected segments or regions, like tandem repeats and transposable elements. Methodologies used include sequence alignment, searches against biological databases, and others.

In bioinformatics, BLAST is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences. A BLAST search enables a researcher to compare a subject protein or nucleotide sequence with a library or database of sequences, and identify database sequences that resemble the query sequence above a certain threshold. For example, following the discovery of a previously unknown gene in the mouse, a scientist will typically perform a BLAST search of the human genome to see if humans carry a similar gene; BLAST will identify sequences in the human genome that resemble the mouse gene based on similarity of sequence.

In computational biology, gene prediction or gene finding refers to the process of identifying the regions of genomic DNA that encode genes. This includes protein-coding genes as well as RNA genes, but may also include prediction of other functional elements such as regulatory regions. Gene finding is one of the first and most important steps in understanding the genome of a species once it has been sequenced.

<span class="mw-page-title-main">Comparative genomics</span> Field of biological research

Comparative genomics is a branch of biological research that examines genome sequences across a spectrum of species, spanning from humans and mice to a diverse array of organisms from bacteria to chimpanzees. This large-scale holistic approach compares two or more genomes to discover the similarities and differences between the genomes and to study the biology of the individual genomes. Comparison of whole genome sequences provides a highly detailed view of how organisms are related to each other at the gene level. By comparing whole genome sequences, researchers gain insights into genetic relationships between organisms and study evolutionary changes. The major principle of comparative genomics is that common features of two organisms will often be encoded within the DNA that is evolutionarily conserved between them. Therefore, Comparative genomics provides a powerful tool for studying evolutionary changes among organisms, helping to identify genes that are conserved or common among species, as well as genes that give unique characteristics of each organism. Moreover, these studies can be performed at different levels of the genomes to obtain multiple perspectives about the organisms.

<span class="mw-page-title-main">Metagenomics</span> Study of genes found in the environment

Metagenomics is the study of genetic material recovered directly from environmental or clinical samples by a method called sequencing. The broad field may also be referred to as environmental genomics, ecogenomics, community genomics or microbiomics.

<span class="mw-page-title-main">Smith–Waterman algorithm</span> Algorithm for determining similar regions between two molecular sequences

The Smith–Waterman algorithm performs local sequence alignment; that is, for determining similar regions between two strings of nucleic acid sequences or protein sequences. Instead of looking at the entire sequence, the Smith–Waterman algorithm compares segments of all possible lengths and optimizes the similarity measure.

Computational genomics refers to the use of computational and statistical analysis to decipher biology from genome sequences and related data, including both DNA and RNA sequence as well as other "post-genomic" data. These, in combination with computational and statistical approaches to understanding the function of the genes and statistical association analysis, this field is also often referred to as Computational and Statistical Genetics/genomics. As such, computational genomics may be regarded as a subset of bioinformatics and computational biology, but with a focus on using whole genomes to understand the principles of how the DNA of a species controls its biology at the molecular level and beyond. With the current abundance of massive biological datasets, computational studies have become one of the most important means to biological discovery.

<span class="mw-page-title-main">CLC bio</span>

CLC bio was a bioinformatics software company that developed a software suite subsequently purchased by QIAGEN.

<span class="mw-page-title-main">David Haussler</span> American bioinformatician

David Haussler is an American bioinformatician known for his work leading the team that assembled the first human genome sequence in the race to complete the Human Genome Project and subsequently for comparative genome analysis that deepens understanding the molecular function and evolution of the genome.

GeneMark is a generic name for a family of ab initio gene prediction algorithms and software programs developed at the Georgia Institute of Technology in Atlanta. Developed in 1993, original GeneMark was used in 1995 as a primary gene prediction tool for annotation of the first completely sequenced bacterial genome of Haemophilus influenzae, and in 1996 for the first archaeal genome of Methanococcus jannaschii. The algorithm introduced inhomogeneous three-periodic Markov chain models of protein-coding DNA sequence that became standard in gene prediction as well as Bayesian approach to gene prediction in two DNA strands simultaneously. Species specific parameters of the models were estimated from training sets of sequences of known type. The major step of the algorithm computes for a given DNA fragment posterior probabilities of either being "protein-coding" in each of six possible reading frames or being "non-coding". The original GeneMark was an HMM-like algorithm; it could be viewed as approximation to known in the HMM theory posterior decoding algorithm for appropriately defined HMM model of DNA sequence.

<span class="mw-page-title-main">HMMER</span> Software package for sequence analysis

HMMER is a free and commonly used software package for sequence analysis written by Sean Eddy. Its general usage is to identify homologous protein or nucleotide sequences, and to perform sequence alignments. It detects homology by comparing a profile-HMM to either a single sequence or a database of sequences. Sequences that score significantly better to the profile-HMM compared to a null model are considered to be homologous to the sequences that were used to construct the profile-HMM. Profile-HMMs are constructed from a multiple sequence alignment in the HMMER package using the hmmbuild program. The profile-HMM implementation used in the HMMER software was based on the work of Krogh and colleagues. HMMER is a console utility ported to every major operating system, including different versions of Linux, Windows, and macOS.

In bioinformatics, alignment-free sequence analysis approaches to molecular sequence and structure data provide alternatives over alignment-based approaches.

PatternHunter is a commercially available homology search instrument software that uses sequence alignment techniques. It was initially developed in the year 2002 by three scientists: Bin Ma, John Tramp and Ming Li. These scientists were driven by the desire to solve the problem that many investigators face during studies that involve genomics and proteomics. These scientists realized that such studies greatly relied on homology studies that established short seed matches that were subsequently lengthened. Describing homologous genes was an essential part of most evolutionary studies and was crucial to the understanding of the evolution of gene families, the relationship between domains and families. Homologous genes could only be studied effectively using search tools that established like portions or local placement between two proteins or nucleic acid sequences. Homology was quantified by scores obtained from matching sequences, “mismatch and gap scores”.

Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining.

Non-coding RNAs have been discovered using both experimental and bioinformatic approaches. Bioinformatic approaches can be divided into three main categories. The first involves homology search, although these techniques are by definition unable to find new classes of ncRNAs. The second category includes algorithms designed to discover specific types of ncRNAs that have similar properties. Finally, some discovery methods are based on very general properties of RNA, and are thus able to discover entirely new kinds of ncRNAs.

References

  1. ↑ GenomeWeb staff reporter (26 August 2003). "Reagent Firm Active Motif Acquires TimeLogic". GenomeWeb . Retrieved 20 September 2011.
  2. ↑ "TimeLogic's latest FPGA technology to be coupled with Geneious Server". January 18, 2011. Retrieved 20 November 2011.
  3. ↑ "Active Motif and the CeBiTec Form Partnership to Jointly Develop Computational Tools for TimeLogic Corporation's FPGA-Based Hardware Accelerated Bioinformatics Platform". November 14, 2011. Archived from the original on 14 February 2012. Retrieved 1 December 2011.
  4. ↑ Blattner, F. R.; Plunkett g, G.; Bloch, C. A.; Perna, N. T.; Burland, V.; Riley, M.; Collado-Vides, J.; Glasner, J. D.; Rode, C. K.; Mayhew, G. F.; Gregor, J.; Davis, N. W.; Kirkpatrick, H. A.; Goeden, M. A.; Rose, D. J.; Mau, B.; Shao, Y. (1997). "The Complete Genome Sequence of Escherichia coli K-12". Science. 277 (5331): 1453–1462. doi: 10.1126/science.277.5331.1453 . PMID   9278503.
  5. ↑ Gillis, Justing (August 11, 2005). "Rice Genome Fully Mapped". washingtonpost.com.
  6. ↑ Goff, S. A.; Ricke, D.; Lan, T. H.; Presting, G.; Wang, R.; Dunn, M.; Glazebrook, J.; Sessions, A.; Oeller, P.; Varma, H.; Hadley, D.; Hutchison, D.; Martin, C.; Katagiri, F.; Lange, B. M.; Moughamer, T.; Xia, Y.; Budworth, P.; Zhong, J.; Miguel, T.; Paszkowski, U.; Zhang, S.; Colbert, M.; Sun, W. L.; Chen, L.; Cooper, B.; Park, S.; Wood, T. C.; Mao, L.; Quail, P. (2002). "A Draft Sequence of the Rice Genome (Oryza sativa L. Ssp. Japonica)". Science. 296 (5565): 92–100. Bibcode:2002Sci...296...92G. doi:10.1126/science.1068275. PMID   11935018. S2CID   2960202.
  7. ↑ Scholl, E. H.; Thorne, J. L.; McCarter, J. P.; Bird, D. M. (2003). "Horizontally transferred genes in plant-parasitic nematodes: A high-throughput genomic approach". Genome Biology. 4 (6): R39. doi: 10.1186/gb-2003-4-6-r39 . PMC   193618 . PMID   12801413.
  8. ↑ Yooseph, S.; Sutton, G.; Rusch, D. B.; Halpern, A. L.; Williamson, S. J.; Remington, K.; Eisen, J. A.; Heidelberg, K. B.; Manning, G.; Li, W.; Jaroszewski, L.; Cieplak, P.; Miller, C. S.; Li, H.; Mashiyama, S. T.; Joachimiak, M. P.; Van Belle, C.; Chandonia, J. M.; Soergel, D. A.; Zhai, Y.; Natarajan, K.; Lee, S.; Raphael, B. J.; Bafna, V.; Friedman, R.; Brenner, S. E.; Godzik, A.; Eisenberg, D.; Dixon, J. E.; Taylor, S. S. (2007). "The Sorcerer II Global Ocean Sampling Expedition: Expanding the Universe of Protein Families". PLOS Biology. 5 (3): e16. doi: 10.1371/journal.pbio.0050016 . PMC   1821046 . PMID   17355171.
  9. ↑ Venter, J. Craig (October 18, 2007). A Life Decoded: My Genome: My Life . New York, New York: Viking Adult. ISBN   978-0-670-06358-1. OCLC   165048736.
  10. ↑ Shultz, J. L.; Ali, S.; Ballard, L.; Lightfoot, D. A. (2007). "Development of a physical map of the soybean pathogen Fusarium virguliforme based on synteny with Fusarium graminearum genomic DNA". BMC Genomics. 8: 262. doi: 10.1186/1471-2164-8-262 . PMC   1978504 . PMID   17683537.
  11. ↑ Zhan, B.; Fadista, J.; Thomsen, B.; Hedegaard, J.; Panitz, F.; Bendixen, C. (2011). "Global assessment of genomic variation in cattle by genome resequencing and high-throughput genotyping". BMC Genomics. 12: 557. doi: 10.1186/1471-2164-12-557 . PMC   3248099 . PMID   22082336.
  12. ↑ Birney, E.; Clamp, M.; Durbin, R. (2004). "GeneWise and Genomewise". Genome Research. 14 (5): 988–995. doi:10.1101/gr.1865504. PMC   479130 . PMID   15123596.
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