SuperSAGE array: the direct use of 26-base-pair transcript tags in oligonucleotide arrays

We developed a new platform for genome-wide gene expression analysis in any eukaryotic organism, which we called SuperSAGE array. The SuperSAGE array is a microarray onto which 26-bp oligonucleotides corresponding to SuperSAGE tag sequences are directly synthesized. A SuperSAGE array combines the advantages of the highly quantitative SuperSAGE expression analysis with the high-throughput microarray technology. We demonstrated highly reproducible gene expression profiling by the SuperSAGE array for 1,000 genes (tags) in rice. We also applied this technology to the detailed study of expressed genes identified by SuperSAGE in Nicotiana benthamiana, an organism for which sufficient genome sequence information is not available. We propose that the SuperSAGE array system represents a new paradigm for microarray construction, as no genomic or cDNA sequence data are required for its preparation.     

Exploring Microbial Diversity and Taxonomy Using SSU rRNA Hypervariable Tag Sequencing

Massively parallel pyrosequencing of hypervariable regions from small subunit ribosomal RNA (SSU rRNA) genes can sample a microbial community two or three orders of magnitude more deeply per dollar and per hour than capillary sequencing of full-length SSU rRNA. As with full-length rRNA surveys, each sequence read is a tag surrogate for a single microbe. However, rather than assigning taxonomy by creating gene trees de novo that include all experimental sequences and certain reference taxa, we compare the hypervariable region tags to an extensive database of rRNA sequences and assign taxonomy based on the best match in a Global Alignment for Sequence Taxonomy (GAST) process. The resulting taxonomic census provides information on both composition and diversity of the microbial community. To determine the effectiveness of using only hypervariable region tags for assessing microbial community membership, we compared the taxonomy assigned to the V3 and V6 hypervariable regions with the taxonomy assigned to full-length SSU rRNA sequences isolated from both the human gut and a deep-sea hydrothermal vent. The hypervariable region tags and full-length rRNA sequences provided equivalent taxonomy and measures of relative abundance of microbial communities, even for tags up to 15% divergent from their nearest reference match. The greater sampling depth per dollar afforded by massively parallel pyrosequencing reveals many more members of the “rare biosphere” than does capillary sequencing of the full-length gene. In addition, tag sequencing eliminates cloning bias and the sequences are short enough to be completely sequenced in a single read, maximizing the number of organisms sampled in a run while minimizing chimera formation. This technique allows the cost-effective exploration of changes in microbial community structure, including the rare biosphere, over space and time and can be applied immediately to initiatives, such as the Human Microbiome Project.     

Extract‐SAGE: An integrated platform for cross­analysis and GA­based selection of SAGE data

Serial analysis of gene expression (SAGE) is a powerful quantification technique for gene expression data. The huge amount of tag data in SAGE libraries of samples is difficult to analyze with current SAGE analysis tools. Data is often not provided in a biologically significant way for cross‐analysis and ‐comparison, thus limiting its application. Hence, an integrated software platform that can perform such a complex task is required. Here, we implement set theory for cross‐analyzing gene expression data among different SAGE libraries of tissue sources; up‐ or down‐regulated tissue‐specific tags can be identified computationally. Extract‐SAGE employs a genetic algorithm (GA) to reduce the number of genes among the SAGE libraries. Its representative tag mining will facilitate the discovery of the candidate genes with discriminating gene expression.     

BIPES,a cost-effective high-throughput method for assessing microbial diversity

Pyrosequencing of 16S rRNA (16S) variable tags has become the most popular method for assessing microbial diversity, but the method remains costly for the evaluation of large numbers of environmental samples with high sequencing depths. We developed a barcoded Illumina paired-end (PE) sequencing (BIPES) method that sequences each 16S V6 tag from both ends on the Illumina HiSeq 2000, and the PE reads are then overlapped to obtain the V6 tag. The average accuracy of Illumina single-end (SE) reads was only 97.9%, which decreased from ∼99.9% at the start of the read to less than 85% at the end of the read; nevertheless, overlapping of the PE reads significantly increased the sequencing accuracy to 99.65% by verifying the 3′ end of each SE in which the sequencing quality was degraded. After the removal of tags with two or more mismatches within the medial 40–70 bases of the reads and of tags with any primer errors, the overall base sequencing accuracy of the BIPES reads was further increased to 99.93%. The BIPES reads reflected the amounts of the various tags in the initial template, but long tags and high GC tags were underestimated. The BIPES method yields 20–50 times more 16S V6 tags than does pyrosequencing in a single-flow cell run, and each of the BIPES reads costs less than 1/40 of a pyrosequencing read. As a laborsaving and cost-effective method, BIPES can be routinely used to analyze the microbial ecology of both environmental and human microbiomes.     

Not all sequence tags are created equal: designing and validating sequence identification tags robust to indels

Ligating adapters with unique synthetic oligonucleotide sequences (sequence tags) onto individual DNA samples before massively parallel sequencing is a popular and efficient way to obtain sequence data from many individual samples. Tag sequences should be numerous and sufficiently different to ensure sequencing, replication, and oligonucleotide synthesis errors do not cause tags to be unrecoverable or confused. However, many design approaches only protect against substitution errors during sequencing and extant tag sets contain too few tag sequences. We developed an open-source software package to validate sequence tags for conformance to two distance metrics and design sequence tags robust to indel and substitution errors. We use this software package to evaluate several commercial and non-commercial sequence tag sets, design several large sets (max_count = 7,198) of edit metric sequence tags having different lengths and degrees of error correction, and integrate a subset of these edit metric tags to polymerase chain reaction (PCR) primers and sequencing adapters. We validate a subset of these edit metric tagged PCR primers and sequencing adapters by sequencing on several platforms and subsequent comparison to commercially available alternatives. We find that several commonly used sets of sequence tags or design methodologies used to produce sequence tags do not meet the minimum expectations of their underlying distance metric, and we find that PCR primers and sequencing adapters incorporating edit metric sequence tags designed by our software package perform as well as their commercial counterparts. We suggest that researchers evaluate sequence tags prior to use or evaluate tags that they have been using. The sequence tag sets we design improve on extant sets because they are large, valid across the set, and robust to the suite of substitution, insertion, and deletion errors affecting massively parallel sequencing workflows on all currently used platforms.     

Improved Lower Bounds of DNA Tags Based on a Modified Genetic Algorithm

The well-known massively parallel sequencing method is efficient and it can obtain sequence data from multiple individual samples. In order to ensure that sequencing, replication, and oligonucleotide synthesis errors do not result in tags (or barcodes) that are unrecoverable or confused, the tag sequences should be abundant and sufficiently different. Recently, many design methods have been proposed for correcting errors in data using error-correcting codes. The existing tag sets contain small tag sequences, so we used a modified genetic algorithm to improve the lower bound of the tag sets in this study. Compared with previous research, our algorithm is effective for designing sets of DNA tags. Moreover, the GC content determined by existing methods includes an imprecise range. Thus, we improved the GC content determination method to obtain tag sets that control the GC content in a more precise range. Finally, previous studies have only considered perfect self-complementarity. Thus, we considered the crossover between different tags and introduced an improved constraint into the design of tag sets.     

A high throughput method for genome-wide analysis of retroviral integration

Retroviral and lentiviral vectors integrate their DNA into the host cell genome leading to stable transgene expression. Integration preferentially occurs in the proximity of active genes, and may in some case disturb their activity, with adverse toxic consequences. To efficiently analyze high numbers of lentiviral insertion sites in the DNA of transduced cells, we developed an improved high-throughput method called vector integration tag analysis (VITA). VITA is based on the identification of Genomic Tags associated to the insertion sites, which are used as signatures of the integration events. We use the capacity of MmeI to cleave DNA at a defined distance of its recognition site, in order to generate 21 bp long tags from libraries of junction fragments between vector and cellular DNA. The length of the tags is sufficient in most cases, to identify without ambiguity an unique position in the human genome. Concatenation, cloning and sequencing of the tags allow to obtain information about 20–25 insertion sites in a single sequencing reaction. As a validation of this method, we have characterized 1349 different lentiviral vector insertion sites in transduced HeLa cells, from only 487 sequencing reactions, with a background of <2% false positive tags.     

Parallel tagged sequencing on the 454 platform

Parallel tagged sequencing (PTS) is a molecular barcoding method designed to adapt the recently developed high-throughput 454 parallel sequencing technology for use with multiple samples. Unlike other barcoding methods, PTS can be applied to any type of double-stranded DNA (dsDNA) sample, including shotgun DNA libraries and pools of PCR products, and requires no amplification or gel purification steps. The method relies on attaching sample-specific barcoding adapters, which include sequence tags and a restriction site, to blunt-end repaired DNA samples by ligation and strand-displacement. After pooling multiple barcoded samples, molecules without sequence tags are effectively excluded from sequencing by dephosphorylation and restriction digestion, and using the tag sequences, the source of each DNA sequence can be traced. This protocol allows for sequencing 300 or more complete mitochondrial genomes on a single 454 GS FLX run, or twenty-five 6-kb plasmid sequences on only one 16th plate region. Most of the reactions can be performed in a multichannel setup on 96-well reaction plates, allowing for processing up to several hundreds of samples in a few days.