The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Computational geometry

A compressed full-text self-index represents a text in a compressed form and still answers queries efficiently. This represents a significant advancement over the (full-)text indexing techniques of the previous decade, whose indexes required several times the size of the text. Although it is relatively new, this algorithmic technology has matured up to a point where theoretical research is giving way to practical developments. Nonetheless this requires significant programming skills, a deep engineering effort, and a strong algorithmic background to dig into the research results. To date only isolated implementations and focused comparisons of compressed indexes have been reported, and they missed a common API, which prevented their re-use or deployment within other applications.The goal of this article is to fill this gap. First, we present the existing implementations of compressed indexes from a practitioner's point of view. Second, we introduce the Pizza&Chili site, which offers tuned implementations and a standardized API for the most successful compressed full-text self-indexes, together with effective test-beds and scripts for their automatic validation and test. Third, we show the results of our extensive experiments on these codes with the aim of demonstrating the practical relevance of this novel algorithmic technology.

http://pizzachili.dcc.uchile.cl/resources/compressed_indexes_in_practice.pdf

Compressed text indexes: From theory to practice

AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data warehousing, social data storage and analysis, and other use cases related to Big Data. AsterixDB has a flexible NoSQL style data model; a query language that supports a wide range of queries; a scalable runtime; partitioned, LSM-based data storage and indexing (including B+-tree, R-tree, and text indexes); support for external as well as natively stored data; a rich set of built-in types; support for fuzzy, spatial, and temporal types and queries; a built-in notion of data feeds for ingestion of data; and transaction support akin to that of a NoSQL store. 
Development of AsterixDB began in 2009 and led to a mid-2013 initial open source release. This paper is the first complete description of the resulting open source AsterixDB system. Covered herein are the system's data model, its query language, and its software architecture. Also included are a summary of the current status of the project and a first glimpse into how AsterixDB performs when compared to alternative technologies, including a parallel relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data analytics platform, for things that both technologies can do. Also included is a brief description of some initial trials that the system has undergone and the lessons learned (and plans laid) based on those early "customer" engagements.

AsterixDB: A Scalable, Open Source BDMS

Wavelet trees for all

Variants of Huffman codes where words are taken as the source symbols are currently the most attractive choices to compress natural language text databases. In particular, Tagged Huffman Code by Moura et al. offers fast direct searching on the compressed text and random access capabilities, in exchange for producing around 11% larger compressed files. This work describes End-Tagged Dense Code and (s, c)-Dense Code, two new semistatic statistical methods for compressing natural language texts. These techniques permit simpler and faster encoding and obtain better compression ratios than Tagged Huffman Code, while maintaining its fast direct search and random access capabilities. We show that Dense Codes improve Tagged Huffman Code compression ratio by about 10%, reaching only 0.6% overhead over the optimal Huffman compression ratio. Being simpler, Dense Codes are generated 45% to 60% faster than Huffman codes. This makes Dense Codes a very attractive alternative to Huffman code variants for various reasons: they are simpler to program, faster to build, of almost optimal size, and as fast and easy to search as the best Huffman variants, which are not so close to the optimal size.

Lightweight natural language text compression

We present a new compression format for natural language texts, allowing both exact and approximate search without decompression. This new code -called End-Tagged Dense Code- has some advantages with respect to other compression techniques with similar features such as the Tagged Huffman Code of [Moura et al., ACM TOIS 2000]. Our compression method obtains (i) better compression ratios, (ii) a simpler vocabulary representation, and (iii) a simpler and faster encoding. At the same time, it retains the most interesting features of the method based on the Tagged Huffman Code, i.e., exact search for words and phrases directly on the compressed text using any known sequential pattern matching algorithm, efficient word-based approximate and extended searches without any decoding, and efficient decompression of arbitrary portions of the text. As a side effect, our analytical results give new upper and lower bounds for the redundancy of d-ary Huffman codes.

An efficient compression code for text databases

We address the problem of adaptive compression of natural language text, considering the case where the receiver is much less powerful than the sender, as in mobile applications. Our techniques achieve compression ratios around 32p and require very little effort from the receiver. Furthermore, the receiver is not only lighter, but it can also search the compressed text with less work than that necessary to decompress it. This is a novelty in two senses: it breaks the usual compressor/decompressor symmetry typical of adaptive schemes, and it contradicts the long-standing assumption that only semistatic codes could be searched more efficiently than the uncompressed text. Our novel compression methods are preferable in several aspects over the existing adaptive and semistatic compressors for natural language texts.

/pdf/dynamic-lightweight-text-compression-52akuw19of.pdf

José R. Paramá

Papers

Lightweight natural language text compression

An efficient compression code for text databases

Dynamic lightweight text compression

Scalable and queryable compressed storage structure for raster data

GraCT: A Grammar-based Compressed Index for Trajectory Data