Optimized detection of insertions/deletions (INDELs) in whole-exome sequencing data

<div><p>Insertion and deletion (INDEL) mutations, the most common type of structural variance, are associated with several human diseases. The detection of INDELs through next-generation sequencing (NGS) is becoming more common due to the decrease in costs, the increase in efficiency, and sensitivity improvements demonstrated by the various sequencing platforms and analytical tools. However, there are still many errors associated with INDEL variant calling, and distinguishing INDELs from errors in NGS remains challenging. To evaluate INDEL calling from whole-exome sequencing (WES) data, we performed Sanger sequencing for all INDELs called from the several calling algorithm. We compared the performance of the four algorithms (<i>i</i>.<i>e</i>. GATK, SAMtools, Dindel, and Freebayes) for INDEL detection from the same sample. We examined the sensitivity and PPV of GATK (90.2 and 89.5%, respectively), SAMtools (75.3 and 94.4%, respectively), Dindel (90.1 and 88.6%, respectively), and Freebayes (80.1 and 94.4%, respectively). GATK had the highest sensitivity. Furthermore, we identified INDELs with high PPV (4 algorithms intersection: 98.7%, 3 algorithms intersection: 97.6%, and GATK and SAMtools intersection INDELs: 97.6%). We presented two key sources of difficulties in accurate INDEL detection: 1) the presence of repeat, and 2) heterozygous INDELs. Herein we could suggest the accessible algorithms that selectively reduce error rates and thereby facilitate INDEL detection. Our study may also serve as a basis for understanding the accuracy and completeness of INDEL detection.</p></div

Validation of the four algorithms used for INDEL calling with WES and Sanger sequencing.

<p>Validation of the four algorithms used for INDEL calling with WES and Sanger sequencing.</p

Sources of INDEL detection error from WES.

<p>(A) Number of validated INDELs in the following INDEL size. (B) Percentages of homozygous, heterozygous, repeat, and non-repeat in the validated and not validated set. (C) PPVs of error sources, 1) heterozygous, 2) repeat INDELs in all and GATK & SAMtools intersecting call set.</p

Comparison of INDEL-calling algorithms.

<p>Comparison of INDEL-calling algorithms.</p

Performance versus detected INDELs and PPVs.

<p>(A) Concordance of INDEL detection between the four algorithms: GATK, SAMtools, Dindel, and Freebayes. Venn diagram showing the numbers and percentages of shared INDELs from the four algorithms: 4 algorithm intersection INDELs, 3 algorithm intersection INDELs, 2 algorithm intersection INDELs, and algorithm-specific INDELs. (B) Validation rates and PPVs of the intersecting INDELs between algorithms. The sensitivity increases at higher intersecting algorithms.</p

Patterns of Gene Expression Associated with <i>Pten</i> Deficiency in the Developing Inner Ear

<div><p>In inner ear development, phosphatase and tensin homolog (PTEN) is necessary for neuronal maintenance, such as neuronal survival and accurate nerve innervations of hair cells. We previously reported that <i>Pten</i> conditional knockout (cKO) mice exhibited disorganized fasciculus with neuronal apoptosis in spiral ganglion neurons (SGNs). To better understand the genes and signaling networks related to auditory neuron maintenance, we compared the profiles of differentially expressed genes (DEGs) using microarray analysis of the inner ear in E14.5 <i>Pten</i> cKO and wild-type mice. We identified 46 statistically significant transcripts using significance analysis of microarrays, with the false-discovery rate set at 0%. Among the DEGs, expression levels of candidate genes and expression domains were validated by quantitative real-time RT-PCR and <i>in situ</i> hybridization, respectively. Ingenuity pathway analysis using DEGs identified significant signaling networks associated with apoptosis, cellular movement, and axon guidance (i.e., secreted phosphoprotein 1 (<i>Spp1</i>)-mediated cellular movement and regulator of G-protein signaling 4 (<i>Rgs4</i>)-mediated axon guidance). This result was consistent with the phenotypic defects of SGNs in <i>Pten</i> cKO mice (e.g., neuronal apoptosis, abnormal migration, and irregular nerve fiber patterns of SGNs). From this study, we suggest two key regulatory signaling networks mediated by <i>Spp1</i> and <i>Rgs4</i>, which may play potential roles in neuronal differentiation of developing auditory neurons.</p></div

Genes selected for validation of microarray data by qRT-PCR.

<p>Genes selected for validation of microarray data by qRT-PCR.</p

Differentially expressed genes in wild-type and <i>Pten</i> cKO mice at E14.5.

<p>Differentially expressed genes in wild-type and <i>Pten</i> cKO mice at E14.5.</p

Microarray analysis identifies novel <i>Pten</i> targets.

<p>Heat maps for relative gene expression of interest (FDR = 0) obtained from three microarrays comparing <i>Pten</i> cKO to wild-type embryos. Green and red indicate decreased and increased expression, respectively, in <i>Pten</i> cKO mice.</p

Functional network analysis associated with <i>Pten</i>-deficient inner ear.

<p>Network analysis using the Ingenuity Pathway Analysis (IPA) software was conducted using selected genes that were differentially expressed and their close relationships. IPA results show two core networks consisted of <i>Spp1</i>-(red line) and <i>Rgs4</i>-associated interactions (blue line). Genes that were differentially expressed are indicated in pink, and predicted interacting genes (not contained in the microarray data) are indicated in white. Axon guidance signaling pathway-related genes are outlined in magenta. Molecular interactions between connected genes represent direct (solid line) or indirect (dotted line) functional relationships based on the IPA database. Green indicates negative fold changes, while red denotes positive fold changes, according to color intensity.</p