Pindel finds insertions with maximum length of ReadLength - 20, if it needs Long insertions bases to align to the reference. How to detect false positive Lobg in the output of pindel tool? However, there remains insertlons lack of practical methods to detect and assemble long variants. Trends Genet. No heterozygous insertions were predicted. Figure 5 shows an example of CLICs that link insertions Long insertions a distal cancer gene. We then quantify whether these insertions influence the expression of gene g i by calculating a two-sample t -statistic between samples with and without insertions, resulting in score. Bet proteins promote efficient murine leukemia virus integration at Breast inplant bras start sites.
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I am using pindel to detect long insertions, but it doesn't detect anything in any of my samples.
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I am using pindel to detect long insertions, but it doesn't detect anything in any of my samples. However it detects inversions when I say to it not to put it. Why could be this? I called it in samples where I know there are long insertions.
Pindel finds insertions with maximum length of ReadLength - 20, if it needs some bases to align to the reference. Log In. Welcome to Biostar! Please log in to add an answer. I've been spending quite some time on following problem: I sequenced a bacterial genome using pai Dear All! I have a fastq experiment with some tandem duplication on a gene.
I expet to have 30 How to detect false positive deletions in the output of pindel tool? I am using Pindel to have i However it Hi, Please suggest me some tools which can detect large indels esp. Some o Hello everybody, I used bam2pindel b2p with pindel then only pindel without b2P and I don't ob Hi All, I am running pindel on some nextgen data for exploring ins, del, rpl, tandem:dup, etc.
Hello biostars, I wanted to know if any of you has had any experience with software detecting ve I was using Pindel Hey I am working with the tool Pindel to analyse bwa sequenced reads for insertions and deletions I've got a dataset with normal and two tumor samples from the same tumor for each patient.
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Inria - MindTheGap: integrated detection and assembly of short and long insertions
Motivation: Insertions play an important role in genome evolution. However, such variants are difficult to detect from short-read sequencing data, especially when they exceed the paired-end insert size. Many approaches have been proposed to call short insertion variants based on paired-end mapping. However, there remains a lack of practical methods to detect and assemble long variants.
Results: We propose here an original method, called M ind T he G ap , for the integrated detection and assembly of insertion variants from re-sequencing data. Importantly, it is designed to call insertions of any size, whether they are novel or duplicated, homozygous or heterozygous in the donor genome.
M ind T he G ap uses an efficient k -mer-based method to detect insertion sites in a reference genome, and subsequently assemble them from the donor reads. M ind T he G ap showed high recall and precision on simulated datasets of various genome complexities. Contact: rf. Structural variants SVs are large-scale structural changes in the genome.
They have been typically defined in opposition to point mutations, which are single nucleotide polymorphisms SNPs and short insertions or deletions indels. SVs therefore include insertions, deletions and inversions of genomic sequences. Recent research has shown that they play an important role in evolution and diseases Genomes Project Consortium et al.
However, SVs are challenging to discover using present-day sequencing approaches, as they generally span genomic regions that are longer than the reads. Computational methods have been designed to extract evidence of SVs from sequencing data using two types of analyses: paired-end mapping of reads to a reference genome and copy number estimation using read depth Alkan et al.
In this work, we will focus on insertion variants: sequences that are present at one site position in the donor genome but are absent from the reference genome at this site. We divide insertions into three mutually exclusive types: i novel insertions in the donor genome that have no match in the reference, ii duplicated insertions , which are found at two or more sites in the donor and a strict subset of those in the reference and iii transpositions , which are sequences in the reference that moved to a different site in the donor.
Duplicated insertions include mobile element insertions MEI , for which databases of known sequences have been created to facilitate discovery Stewart et al. All three types of insertions are difficult to detect using short reads.
Different techniques are used to detect insertions that are short shorter than the reads , medium of size between read length and insert size or long of size exceeding insert size.
In the next two sections, we review techniques used to identify insertion sites, and techniques used to reconstruct insertion sequences. As short insertions are likely to be fully contained in several reads, mapping donor reads to a reference genome enables simultaneous discovery of the sites and contents of insertions Albers et al.
In this context, results are sensitive to mapping parameters and may be degraded in low-coverage or low-complexity regions of the reference. Although the discovery of short indels has been an extensively studied problem, a recent article has observed considerable differences between the results of popular tools Pabinger et al. Sites of medium-sized insertions can be detected by analyzing mapping positions of paired reads. General SV calling tools call insertions sites by clustering neighboring read pairs that have a shorter insert size than expected, e.
NovelSeq Hajirasouliha et al. Alternatively, tools based on read coverage can detect duplicated insertions of any length by finding reference segments that have higher read depth than expected. While insertion sites cannot be determined by this method alone, the Reprever Kim et al. Finally, several methods detect sites of mobile element insertions using collections of known transposable element sequences, by searching for read pairs where one mate is mapped to a known element and the other to a unique part of the reference genome Ewing and Kazazian, ; Hormozdiari et al.
While short insertions are easy to reconstruct as seen in Section 1. They are based on global or local de novo assembly of reads that are potentially involved in an insertion.
SOAPindel Li et al. The other mates are used to assemble separately each inserted sequence. This approach can only reconstruct insertions that are shorter than twice the insert size. Parrish et al. Insertions appear in the graph as bubbles sets of paths between two nodes , where one short path corresponds to the reference genome, and longer paths correspond to inserted sequences.
Theoretically, this approach enables the discovery of insertions regardless of their size and type. To summarize, available tools are highly specialized and lack the versatility to detect and assemble insertions of any size and any type.
We propose a new tool, M ind T he G ap , for detecting and assembling insertions. M ind T he G ap has several novel features that are not found in other tools. First, a mapping-free site detection algorithm has been designed to detect insertions of any size. Second, an improved method for insertion assembly enables the reconstruction of long insertions of all three types.
Third, a memory-efficient data structure enables high scalability. We evaluated M ind T he G ap on simulated and real Illumina sequencing data. Among 1 kbp simulated homozygous insertions, a large fraction were found and correctly assembled recall values between 65— We assembled long insertions using M ind T he G ap on an actual whole-genome human dataset, which required only 14 GB of memory. The input of M ind T he G ap is a set of reads and a reference genome. The software performs three steps: i construction of the de Bruijn graph of the reads, ii detection of insertion breakpoints on the reference genome find module and iii local assembly of inserted sequences fill module.
Both the detection step and the assembly step rely solely on the constructed graph. The output of the second step is a set of putative insertion positions on the reference genome, whereas the output of the last step is, for each insertion site, one or several assembled sequences.
The de Bruijn graph is a directed graph over all distinct k -mers in the reads. The graph is constructed using the algorithms implemented in the Minia assembler Chikhi and Rizk, ; Salikhov et al. Minia encodes the graph using a Bloom filter and an additional hash table to suppress false-positive results. The data structure supports two operations: i membership queries for k -mers that are neighbors of existing k -mers in the graph, and ii traversal of the graph from an existing k -mer.
These operations are respectively used in Section 2. M ind T he G ap detects insertion sites by scanning the reference genome and testing membership of reference k -mers in the de Bruijn graph. Homozygous and heterozygous insertions are handled using two different methods. The general case for detecting homozygous insertions can be modeled as follows.
Let Sr be a sequence the reference. Depending on the context, the reference genome will correspond to the string of nucleotides or to the string of binary characters.
We refer to this situation as a canonical insertion site see Fig. A Canonical insertion site. B Fuzzy insertion site.
Insertion ends with the same nucleotides TG present on the left of the site. In dashed lines, an alternative insertion site. C Heterozygous insertion site. Flanking k -mers in black surrounding a heterozygous site respectively have two right branching k -mers for the k -mer on the left of the site and two left-branching k -mers for the right k -mer.
We refer to such sites as fuzzy sites. The size of the gap is an important criterion to detect homozygous insertion sites, as other types of variants also yield gaps. Variants that are separated by less than k nucleotides yield longer gaps. Finally, gaps of various sizes may also appear due to insufficient read coverage or non-uniqueness of k -mers inside the reference genome.
These effects are controlled by the value of k , which is a parameter of our method. While heterozygous insertions sites do not yield gaps, flanking k -mers at these sites still exhibit features that can be detected. The left flanking k -mer of a heterozygous insertion site has at least two out-neighbors in the de Bruijn graph: one neighbor in the reference sequence and at least one other neighbor that is a prefix of the inserted sequence. Similarly, the right flanking k -mer has at least two in-neighbors with similar properties see Fig.
As in the homozygous case, small repetitions at the extremities of inserted sequences slightly alter the pattern. The left flanking k -mer may overlap the right flanking k -mer in the reference genome. MindTheGap detects heterozygous insertion sites by scanning the reference genome and testing neighborhoods of putative left and right flanking k -mers whose distance from one another is comprised between k — r and k , r being the same user-defined parameter as for homozygous insertions, indicating the largest allowed repeat at the insertion.
However, heterozygous inversions and translocations do exhibit identical patterns. Also, inexact repetitions in the reference genome create branching k -mers, which may yield by chance the same pattern as a heterozygous insertion. When h is set to 1, this prevents the detection of patterns that may be generated by repetitions in the reference genome alone, in absence of any sequence variants. The third step of M ind T he G ap is called the fill module.
Starting from a known insertion site represented by flanking k -mers L , R , the module performs de novo assembly to attempt to reconstruct the inserted sequence between L and R.
In a nutshell, a graph of contigs is constructed by performing breadth-first traversal of k -mers, starting from L. The traversal is halted when graph becomes too complex. Then, all the contigs in the graph are searched for the presence of R. All paths between L and the contigs containing R are enumerated, and one or more putative insertion sequences are returned see Fig. Fill module. A graph of contig is constructed from the left flanking kmer L , in a breadth-first search order.
Construction stops when a maximum number of nodes is reached, or when a branch becomes too deep. The right flanking kmer R is searched within all nodes, finally all paths in blue between L and R are outputted as putative insertions. More specifically, insertions are assembled using the algorithm of Minia Chikhi and Rizk, ; Salikhov et al. Assembly is performed by traversing the graph from a given starting k -mer in a breadth-first fashion.
A consensus sequence contig is generated by skipping over certain motifs, such as bubbles putative short variants and tips putative errors. This Minia assembly procedure stops whenever a contig cannot be unambiguously extended. A graph of contigs is constructed for each insertion site L , R as follows. First, an initial contig c L is constructed by calling the Minia assembly procedure from the L k -mer. If no neighbor is present, indicating that c could not be extended, then no further action is performed for this contig.