Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1093/bioinformatics/btv399 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btv399 C4838057!4838057 !26139636     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26139636 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Bioinformatics 2015 ; 31 (21 ): 3468-75 Nephropedia Template TP {{tp|p=26139636 |t=2015. ERGC: an efficient referential genome compression algorithm |pdf=|usr=}}{{26139636 }} gab.com Text ERGC: an efficient referential genome compression algorithm JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=26139636 #FOAvip MOTIVATION: Genome sequencing has become faster and more affordable. Consequently, the number of available complete genomic sequences is increasing rapidly. As a result, the cost to store, process, analyze and transmit the data is becoming a bottleneck for research and future medical applications. So, the need for devising efficient data compression and data reduction techniques for biological sequencing data is growing by the day. Although there exists a number of standard data compression algorithms, they are not efficient in compressing biological data. These generic algorithms do not exploit some inherent properties of the sequencing data while compressing. To exploit statistical and information-theoretic properties of genomic sequences, we need specialized compression algorithms. Five different next-generation sequencing data compression problems have been identified and studied in the literature. We propose a novel algorithm for one of these problems known as reference-based genome compression. RESULTS: We have done extensive experiments using five real sequencing datasets. The results on real genomes show that our proposed algorithm is indeed competitive and performs better than the best known algorithms for this problem. It achieves compression ratios that are better than those of the currently best performing algorithms. The time to compress and decompress the whole genome is also very promising. AVAILABILITY AND IMPLEMENTATION: The implementations are freely available for non-commercial purposes. They can be downloaded from http://engr.uconn.edu/?rajasek/ERGC.zip. CONTACT: rajasek@engr.uconn.edu. Twit Text FOAVip ERGC: an efficient referential genome compression algorithm ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=26139636 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26139636 #FOAvip English Wikipedia <ref>{{Cite pmid|26139636 }}</ref> ERGC: an efficient referential genome compression algorithm #MMPMID26139636 Saha S ; Rajasekaran S Bioinformatics 2015[Nov]; 31 (21 ): 3468-75 PMID26139636 show ga MOTIVATION: Genome sequencing has become faster and more affordable. Consequently, the number of available complete genomic sequences is increasing rapidly. As a result, the cost to store, process, analyze and transmit the data is becoming a bottleneck for research and future medical applications. So, the need for devising efficient data compression and data reduction techniques for biological sequencing data is growing by the day. Although there exists a number of standard data compression algorithms, they are not efficient in compressing biological data. These generic algorithms do not exploit some inherent properties of the sequencing data while compressing. To exploit statistical and information-theoretic properties of genomic sequences, we need specialized compression algorithms. Five different next-generation sequencing data compression problems have been identified and studied in the literature. We propose a novel algorithm for one of these problems known as reference-based genome compression. RESULTS: We have done extensive experiments using five real sequencing datasets. The results on real genomes show that our proposed algorithm is indeed competitive and performs better than the best known algorithms for this problem. It achieves compression ratios that are better than those of the currently best performing algorithms. The time to compress and decompress the whole genome is also very promising. AVAILABILITY AND IMPLEMENTATION: The implementations are freely available for non-commercial purposes. They can be downloaded from http://engr.uconn.edu/?rajasek/ERGC.zip. CONTACT: rajasek@engr.uconn.edu. |*Algorithms [MESH] |*Genome, Human [MESH] |Chromosome Mapping [MESH] |Data Compression/*methods [MESH] |Databases, Factual [MESH] |Genomics/*methods [MESH] |High-Throughput Nucleotide Sequencing/*methods [MESH] |Humans [MESH] |Information Storage and Retrieval [MESH]ERGC: an efficient referential genome compression algorithm (epmc).pdf DeepDyve Pubget Overpricing