BMC Genomics 2009, 10:415 http://www.biomedcentral.com/1471-2164/10/415 analysis revealed few canonical genes, several ubiquitous genes across different stresses while most of the genes were found to be non-cannonical. An interesting observa- tion was that apart from metabolism, most of the nonca- nonical genes belonged to the functional classes of translation, posttranslational modifications, transcrip- tion, and signaling suggesting the occurrence of new, yet undiscovered immune response pathways. This study thus provides a comprehensive catalogue of non-canoni- cal immune responsive genes or might suggest their spe- cies specificity with new insight into their identity and function. Identification of gene families To assign CaUnigenes from this study to putative gene families, we used single linkage clustering. The individual contigs and singletons were combined into a single data- set which was then compared to itself using TBLASTX with an e-value cutoff of 1e-15. Sequences with overlapping BLAST reports were assigned to a putative gene family. Altogether, we identified 209 gene families ranging in size from 2 to 29 members (see Additional file 4). This analy- sis gives insights into following three areas: firstly, it iden- tifies the genes that are likely to cross hybridize during the microarray hybridizations. Secondly, it helps in assigning possible function to genes that had no significant homol- ogy to known proteins or belonged to class of UF but clus- tered with proteins of known function. For example, gene family 23 had two members of which one showed homol- ogy to CAP protein while the other showed homology to hypothetical protein. Similarly, one of the members of gene family 32 showed homology to cinnamoyl-CoA reductase, while the other two belonged to NSH class. Also, one member of gene family 124 showed homology with phosphoesterase but the other did not have any sig- nificant match. Therefore, on the basis of the family to which such genes either of NSH or UF belong, possible function can be assigned to them and further verified by comparing sequence alignment of these sequences with representative members of the known gene family. Thirdly, the identification of gene families provides a base for uncovering and understanding the biological rationale of functional novelty and partitioning following gene duplications. Towards this, it was interesting to note that in several cases the distribution of different members of the same gene family varied between the genotypes. For example, histone deacetylase HDT1 (contig212) was found to be specific to susceptible genotype while the other member of the same gene family represented by contig713 was present only in the resistant genotype. This was also the case with methionine sulfoxide reductase A, where contig152 and contig555 were specific to suscepti- ble and resistant genotypes respectively. Putative esterase family protein (CaF1_JIE_28_D_10, CaF1_WIE_32_H_04) and putative desaturase (contig454, CaF1_JIE_22_A_09) also showed the same trend. This opens up a new area for finding out whether presence of different gene family members leads to geno- type specific response to a particular kind of stress. Analysis of genotype-specific SNPs of chickpea SNPs between genotypes/haplotypes once discovered are extensively used for many applications for instance gener- ation of very dense genetic maps, to construct the specific genotypes required for quantitative genetic studies, to enhance understanding of genome organization and function and address fundamental questions relating to evolution. SNPs can also be used for genome-wide linkage disequilibrium and association studies that assign genes to specific functions or traits. Furthermore, transcript- associated SNPs can be used to develop allele-specific assays for the examination of cis regulatory variation within a species [79]. In the present study, SNPs were identified between JG-62, a susceptible and WR-315, a resistant genotype of chickpea to vascular wilt. A total of 279 contigs (28.67% of the total) contained at least one sequence from both the genotypes and were mined for potential SNPs. We identified 262 SNPs which were fur- ther classified into high and low quality SNPs depending upon the number of sequences from each genotype show- ing the same base change (see Additional file 5). High- quality SNPs were confirmed by two or more sequences from each genotype showing the same base change, while low-quality SNPs were confirmed by one sequence from one genotype and at least two from the other. Thus, we identified 136 high-quality and 126 low-quality SNPs. In the present analysis only base pair mutations were taken into consideration and among these transitions (73.3%) were more common than transversions (26.7%). Within the transitions, occurrence of both adenine to guanine and cytosine to thymidine base changes were found to be almost equal. The maximum number of SNPs were present in contigs that comprised of highly abundant ESTs, for example, contig 722 represents the most abun- dant contig and maximum number of SNPs were present in this contig. Similarly, contig 680 and 575 also had high percentage of SNPs and were also very abundant as far as total number of ESTs in these contigs is concerned. Immune responsive gene networks in root transcriptome of chickpea by microarray analysis To study the transcriptional remodeling in immune response during vascular wilt, we have developed cDNA microarray using CaEST clones of the subtracted cDNA libraries from susceptible and resistant genotypes. Root tissue sample from WR-315 (resistant genotype) har- vested at 24 h post Fusarium infection was used to evaluate the expression profile during early phase of incompatible interaction. We used indirect labeling of cDNAs following TSA protocol that incorporates flourscein and biotin Page 10 of 21 (page number not for citation purposes)