Synthetic DNA can in principle be used for the archival storage of arbitrary data. Because errors are introduced during DNA synthesis, storage, and sequencing, an error-correcting code (ECC) is necessary for error-free recovery of the data. Previous work has utilized ECCs that can correct substitution errors, but not insertion or deletion errors (indels), instead relying on sequencing depth and multiple alignment to detect and correct indels -- in effect an inefficient multiple-repetition code. This paper describes an ECC, termed "HEDGES", that corrects simultaneously for substitutions, insertions, and deletions in a single read. Varying code rates allow for correction of up to 10 nucleotide errors and achieve 50 or better of the estimated Shannon limit.
In the largest-scale experiment to date, Organick et. al @cite_5 encoded and recovered, error-free, more than 200 MB of data. R-S coding was used across strands, with no explicit error correction within a strand. Substitutions and indels within a strand were corrected by multiple alignment and consensus calling. Coverage was 5x for high-quality Illumina sequencing, rising to 36x to 80x required for Nanopore technology.
@cite_5
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The emergence of next-generation sequencing platforms led to resurgence of research in whole-genome shotgun assembly algorithms and software. DNA sequencing data from the Roche 454, Illumina Solexa, and ABI SOLiD platforms typically present shorter read lengths, higher coverage, and different error profiles compared with Sanger sequencing data. Since 2005, several assembly software packages have been created or revised specifically for de novo assembly of next-generation sequencing data. This review summarizes and compares the published descriptions of packages named SSAKE, SHARCGS, VCAKE, Newbler, Celera Assembler, Euler, Velvet, ABySS, AllPaths, and SOAPdenovo. More generally, it compares the two standard methods known as the de Bruijn graph approach and the overlap layout consensus approach to assembly. New sequencing technology has dramatically altered the landscape of whole-genome sequencing, allowing scientists to initiate numerous projects to decode the genomes of previously unsequenced organisms. The lowest-cost technology can generate deep coverage of most species, including mammals, in just a few days. The sequence data generated by one of these projects consist of millions or billions of short DNA sequences (reads) that range from 50 to 150 nt in length. These sequences must then be assembled de novo before most genome analyses can begin. Unfortunately, genome assembly remains a very difficult problem, made more difficult by shorter reads and unreliable long-range linking information. In this study, we evaluated several of the leading de novo assembly algorithms on four different short-read data sets, all generated by Illumina sequencers. Our results describe the relative performance of the different assemblers as well as other significant differences in assembly difficulty that appear to be inherent in the genomes themselves. Three overarching conclusions are apparent: first, that data quality, rather than the assembler itself, has a dramatic effect on the quality of an assembled genome; second, that the degree of contiguity of an assembly varies enormously among different assemblers and different genomes; and third, that the correctness of an assembly also varies widely and is not well correlated with statistics on contiguity. To enable others to replicate our results, all of our data and methods are freely available, as are all assemblers used in this study. In this study, we demonstrate the use of natural language processing methods to extract, from nanomedicine literature, numeric values of biomedical property terms of poly(amidoamine) dendrimers. We have developed a method for extracting these values for properties taken from the NanoParticle Ontology, using the General Architecture for Text Engineering and a Nearly-New Information Extraction System. We also created a method for associating the identified numeric values with their corresponding dendrimer properties, called NanoSifter. We demonstrate that our system can correctly extract numeric values of dendrimer properties reported in the cancer treatment literature with high recall, precision, and f-measure. The micro-averaged recall was 0.99, precision was 0.84, and f-measure was 0.91. Similarly, the macro-averaged recall was 0.99, precision was 0.87, and f-measure was 0.92. To our knowledge, these results are the first application of text mining to extract and associate dendrimer property terms and their corresponding numeric values. Abstract The ubiquity of next-generation sequencing has transformed the size and nature of many databases, pushing the boundaries of current indexing and searching methods. One particular example is a database of 2652 human RNA-seq experiments uploaded to the Sequence Read Archive (SRA). Recently, Solomon and Kingsford proposed the Sequence Bloom Tree data structure and demonstrated how it can be used to accurately identify SRA samples that have a transcript of interest potentially expressed. In this article, we propose an improvement called the AllSome Sequence Bloom Tree. Results show that our new data structure significantly improves performance, reducing the tree construction time by 52.7 and query time by 39 –85 , with a price of upto 3 × memory consumption during queries. Notably, it can query a batch of 198,074 queries in <8 hours (compared with around 2 days previously) and a whole set of k-mers from a sequencing experiment (about 27 million k-mers) in <11 minutes.