Chimerism analysis using next generation sequencing

Hematopoietic stem cell transplantation (HSCT) is the predominant curative treatment for many malignant and non-malignant haematological diseases. Early detection of graft rejection and disease relapse following HSCT improves patient outcomes by allowing treatment to be initiated as quickly as possible. In order to evaluate the level of donor engraftment, mixed chimerism levels must be carefully monitored after transplantation. Short-tandem repeat (STR) genotyping is widely used to determine the proportions of donor and recipient cells after HSCT.In this study, Devyser Chimerism NGS kit in combination with a MiSeq System was introduced in our laboratory for monitoring HSCT. This system is a complete workflow solution for labs, combining a reliable testing process with a designed for-purpose analytical software. Up to 24 informative markers in a recipient/donor pair distributed through the human genome have strong discriminative power with low bias from ethnic parameters. These IND/DEL genetic markers with population independent discriminative power are distributed across 17 chromosomes and were further selected to allow sensitive detection combined with accurate and precise quantification of mixed chimerism.Streamlined, simple and robust NGS workflow uses just one multiplex PCR reaction per patient sample. Minimal hands-on time reduces assay complexity and risk of sample contamination and mix-up. User-friendly, designed-for-purpose software perfectly complements testing kit with an automatic detection of informative markers.Insertion/deletion (indel) polymorphisms have been used in the fields of forensic investigations owing to the advantages of their low mutation rates, widespread distributions in the human genome and small amplicon sizes. Thus, forensic efficiency evaluation of this system for forensic individual identification will be also tested.     

Combined analysis of two different ancestry informative assays using SNPs and Indels in Eurasian populations

In the absence of a suspect or DNA database match, small multiplex assays with ancestry informative markers (AIMs) provide an alternative to comparative DNA analysis as the knowledge of an unknown stain donor's biogeographic ancestry can be helpful in guiding criminal investigations. AIMs can provide valuable information in such cases. The research focus for AIMs has been on multiplex assays of single nucleotide polymorphisms (SNPs) and insertion/deletion polymorphisms (Indels). This work presents a combined analysis of two different AIM assays to increase differentiation between Eurasian populations.     

Introduction of the python script MHinNGS for analysis of microhaplotypes

MHinNGS is a Python application developed for analysis of microhaplotypes (MHs) in single-end sequencing data. MHinNGS analyses reads in standard formats and store each sequence into bins, one bin for each MH as defined by the two flanking sequences. MHinNGS requires a reference genome and a configuration file with information about each locus. Four mandatory and 15 optional criteria defined in the configuration file allow detailed locus-specific analyses of the MH loci. The program 1) removes noise, 2) identify and name alleles, 3) test the genotypes, and 4) test unique sequences not identified as noise or alleles. MHinNGS produces a result file, where every unique sequence that passed the noise filter is presented with MH allele, read depth, warning flags based on the genotyping criteria, sequence, heterozygote balance, and MH name. Furthermore, variation in other parts of the fragment that is not defined as SNPs in the MH, linked variants, or rare SNPs are listed in a separate column of the result file.     

Comparative analysis of two indel-based ancestry informative multiplex PCR typing kits

Two different insertion/deletion (indel) multiplexes have been used to analyse a subset of the CEPH Human Genome Diversity Panel as well as several additional populations collected locally in order to compare the effectiveness of the marker sets in differentiating the populations on a continental level. We show that both marker sets by themselves are able to achieve full continental population differentiation and combining all 67 markers leads to considerably higher accuracy of classification. While differentiation of the European and Middle Eastern population groups remains impossible, surprisingly high accuracy is achieved in assigning Central South Asian samples.