Try out PMC Labs and tell us what you think. Learn More. Availability and Implementation: This open-source application was implemented in Perl and can be used as a stand alone version or accessed online through a user-friendly web interface.
Contact: ude. High-throughput sequencing has revolutionized microbiology and accelerated genomic and metagenomic analyses; however, downstream sequence analysis is compromised by low-quality sequences, sequence artifacts and sequence contamination, eventually leading to misassembly and erroneous conclusions.
These problems necessitate better tools for quality control and preprocessing of all sequence datasets. For most next-generation sequence datasets, the quality control should include the investigation of length, GC content, quality score and sequence complexity distributions; sequence duplication; contamination; artifacts; and number of ambiguous bases.
In the preprocessing step, the sequence ends should be trimmed and unwanted sequences should be filtered. The program is publicly available through a user-friendly web interface and as a stand alone version. The web interface allows online analysis and data export for subsequent analysis. The sequence complexity is evaluated as the mean of complexity values using a window of size 64 and a step size of Both use overlapping nucleotide triplets as words and are scaled to a maximum value of The second method evaluates the block-entropies of words using the Shannon—Wiener method:.
The basic version of the dinucleotide odds ratio calculation Burge et al. In addition, the commonly used version that accounts for the complementary antiparallel structure of double-stranded DNA introduces an additional dinucleotide by simply concatenating the sequence with its reverse complement.
To account for this, the odds ratios are calculated using the number n X of nucleotide X and the number n XY of dinucleotide XY only for nucleotides A, C, G and T on the forward strand:. Tag sequences are artifacts at the sequence ends such as adapter or barcode sequence. The k -mers are aligned and shifted before calculating the frequencies as described in Schmieder et al. Initially, we simulate a sequencing dataset with the same length distribution and nucleotide composition as the input dataset.
Finally, we obtain P -values from this null distribution and adjust them using the Benjamini—Hochberg method to reflect the controlled false discovery rate FDR; Benjamini and Hochberg, The lowest sequence coverage that gives the requested FDR is then used as the cutoff value.
For efficiency, TagDust is implemented in the C programming language. Hence, it is applicable to current datasets and the large volume of data expected with future next-generation sequencing instruments. A computational bottleneck is the calculation of the adjusted P -values since this step, in principle, requires sorting of millions of P -values. However, since sequence lengths are natural numbers, only a selection of coverage cutoffs and associated P -values is possible.
We take advantage of this and use a bit-sort-like algorithm to perform this step in linear memory and time. Obtaining suitable datasets for benchmarking our method is not trivial since partially failed sequencing runs are commonly not deposited in public databases. We used the standard Illumina adaptors and primers used in the different sequencing assays as target sequences to be filtered out from the reads. As expected, only a relatively small percentage of the deposited reads can be explained by library sequences Table 1.
To determine whether the same sequences could be filtered out by simply mapping to the reference genome, we mapped all artifactual sequences with up to two mismatches to the human genome hg18 assembly using nexalign T. Lassmann, manuscript in preparation. Evidently, a varying percentage of the artifactual sequences map to the genome. In the absence of replicates it is difficult to determine whether such tags are actual artifacts and hence we recommend users to merely flag such reads and their mapping positions.
Percentages of reads identified as artifacts in five sequencing runs at varying FDR thresholds. The mapping rates of the artifactual sequences to the human genome are indicated in brackets.
Conceivably, the time it takes to map libraries can be reduced by using TagDust to filter out artifacts before the mapping. The two main applications for TagDust are to troubleshoot failed large-scaled sequencing runs and to filter out artifactual sequences from successful ones. The latter may affect the biological interpretation of the produced data since some artifactual sequences map to the respective reference genomes.
Google Scholar. Google Preview. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Sign In. Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Abstract. TagDust—a program to eliminate artifacts from next generation sequencing data.
Traditional sequence alignment software like blast Altschul et al. To our knowledge, there have been several programs developed or under developing to match the new sequencing technologies. ELAND, an alignment tool integrated in Illumina-Solexa data processing package, can do ungapped alignment for reads with size up to 32 bp Cox, unpublished.
Maq is another program for ungapped alignment, which implemented sophisticated probability models to measure alignment quality of each read using sequence quality information Li, unpublished. SOAP will allow either a certain number of mismatches or one continuous gap for aligning a read onto the reference sequence.
The best hit of each read which has minimal number of mismatches or smaller gap will be reported. For multiple equal-best hits, the user can instruct the program to report all, or randomly report one, or disregard all of them.
Since the typical read length is 25—50 bp, hits with too many mismatches are unreliable which are hard to distinguish with random matches. By default, the program will allow at most two mismatches. Between two haplotype genome sequences, occurrence of single nucleotide polymorphism is much higher than that of small insertions or deletions, so ungapped hits have precedence over gapped hits.
For gapped alignment only one continuous gap with a size ranging from 1 to 3 bp is accepted, while no mismatches are permitted in the flanking regions to avoid ambiguous gaps. The gap could be either insertion or deletion in the query or the reference sequence. As the intrinsic character of the sequencing technology, errors will accumulate during the sequencing process.
Pair-end sequencing means to sequence both ends of a DNA fragment. So the two reads belonging to a pair will always have the settled relative orientation and approximate distance between each other on the genome. The technology can significantly improve the accuracy of resequencing mapping, and is a powerful method for detection of structural variants including copy number variations CNVs , rearrangements, inversions and etc.
SOAP is able to align a pair of reads simultaneously. A pair will be aligned when two reads are mapped with the right orientation relationship and proper distance. Similar filter as single-read alignment, a certain number of mismatches are allowed in one or both reads of the pair.
For gapped alignment, gap is only permitted on one read, and the other end should match exactly. Apart from genome resequencing, The high throughput sequencing technology lends itself to numerous applications. For some applications ex. ChIP-Seq , the data analysis process is essentially identical to that of resequencing.
Small RNAs have a size between 18 to 26 bp. A small RNA will be annotated if an adapter sequence is detected and the insert sequence match well with the reference sequence. Considering sequencing errors, one or two mismatches can be allowed insider either the adapter or the candidate RNA region according to user settings.
Aligned hits should contain the enzyme site, and have at most one mismatch in the tag region. Evaluated on a real dataset containing 9 Illumina-Solexa single-end resequencing reads length 32 bp , which were generated from a 5 Mb human genome region, SOAP was almost gapped to ungapped times faster than blastn, while having better sensitivity Table 1.
The iterative feature of SOAP improved sensitivity. And gapped alignment can further identify hits accommodating small indels which compose only a small fraction of all hits but are a very important class of mutation.
Since SOAP loads reference sequences into memory, while Eland and Maq load reads, the memory usage varies in different datasets. Comparison of performance and sensitivity among short oligonucleotide alignment programs. We used a query dataset containing 9 single-end reads length 32 bp generated by Illumina-Solexa Genome Analyzer. For blat, tileSize parameter was set at 8.
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