Reconstruction of haplotype-blocks selected during experimental evolution.

The genetic analysis of experimentally evolving populations typically relies on short reads from pooled individuals (Pool-Seq). While this method provides reliable allele frequency estimates, the underlying haplotype structure remains poorly characterized. With small population sizes and adaptive variants that start from low frequencies, the interpretation of selection signatures in most Evolve and Resequencing studies remains challenging. To facilitate the characterization of selection targets, we propose a new approach that reconstructs selected haplotypes from replicated time series, using Pool-Seq data. We identify selected haplotypes through the correlated frequencies of alleles carried by them. Computer simulations indicate that selected haplotype-blocks of several Mb can be reconstructed with high confidence and low error rates, even when allele frequencies change only by 20% across three replicates. Applying this method to real data from D. melanogaster populations adapting to a hot environment, we identify a selected haplotype-block of 6.93 Mb. We confirm the presence of this haplotype-block in evolved populations by experimental haplotyping, demonstrating the power and accuracy of our haplotype reconstruction from Pool-Seq data. We propose that the combination of allele frequency estimates with haplotype information will provide the key to understanding the dynamics of adaptive alleles

Interpreting and Correcting Medical Image Classification with PIP-Net

Part-prototype models are explainable-by-design image classifiers, and a promising alternative to black box AI. This paper explores the applicability and potential of interpretable machine learning, in particular PIP-Net, for automated diagnosis support on real-world medical imaging data. PIP-Net learns human-understandable prototypical image parts and we evaluate its accuracy and interpretability for fracture detection and skin cancer diagnosis. We find that PIP-Net's decision making process is in line with medical classification standards, while only provided with image-level class labels. Because of PIP-Net's unsupervised pretraining of prototypes, data quality problems such as undesired text in an X-ray or labelling errors can be easily identified. Additionally, we are the first to show that humans can manually correct the reasoning of PIP-Net by directly disabling undesired prototypes. We conclude that part-prototype models are promising for medical applications due to their interpretability and potential for advanced model debugging

Interpreting and Correcting Medical Image Classification with PIP-Net

Part-prototype models are explainable-by-design image classifiers, and a promising alternative to black box AI. This paper explores the applicability and potential of interpretable machine learning, in particular PIP-Net, for automated diagnosis support on real-world medical imaging data. PIP-Net learns human-understandable prototypical image parts and we evaluate its accuracy and interpretability for fracture detection and skin cancer diagnosis. We find that PIP-Net’s decision making process is in line with medical classification standards, while only provided with image-level class labels. Because of PIP-Net’s unsupervised pretraining of prototypes, data quality problems such as undesired text in an X-ray or labelling errors can be easily identified. Additionally, we are the first to show that humans can manually correct the reasoning of PIP-Net by directly disabling undesired prototypes. We conclude that part-prototype models are promising for medical applications due to their interpretability and potential for advanced model debugging.</p

Interpreting and Correcting Medical Image Classification with PIP-Net

Part-prototype models are explainable-by-design image classifiers, and a promising alternative to black box AI. This paper explores the applicability and potential of interpretable machine learning, in particular PIP-Net, for automated diagnosis support on real-world medical imaging data. PIP-Net learns human-understandable prototypical image parts and we evaluate its accuracy and interpretability for fracture detection and skin cancer diagnosis. We find that PIP-Net's decision making process is in line with medical classification standards, while only provided with image-level class labels. Because of PIP-Net's unsupervised pretraining of prototypes, data quality problems such as undesired text in an X-ray or labelling errors can be easily identified. Additionally, we are the first to show that humans can manually correct the reasoning of PIP-Net by directly disabling undesired prototypes. We conclude that part-prototype models are promising for medical applications due to their interpretability and potential for advanced model debugging

Direct estimation of the mutation rate at dinucleotide microsatellite loci in Arabidopsis thaliana (Brassicaceae)

This is the author's accepted manuscript, made available with the permission of the publisher.This research was supported by NIH grant GM073990 and NSF grant DEB-0543052 to J. K. Kelly, NSF grants DEB-9629457 and DEB-9981891 to R. G. Shaw, and NSF DEB-0108242 to M. Orive. M. E. Mort acknowledges DEB-0344883

Genetic diversity and population structure of Ascochyta rabiei from the western Iranian Ilam and Kermanshah provinces using MAT and SSR markers

Knowledge of genetic diversity in A. rabiei provides different levels of information that are important in the management of crop germplasm resources. Gene flow on a regional level indicates a significant potential risk for the regional spread of novel alleles that might contribute to fungicide resistance or the breakdown of resistance genes. Simple sequence repeat (SSR) and mating type (MAT) markers were used to determine the genetic structure, and estimate genetic diversity and the prevalence of mating types in 103 Ascochyta rabiei isolates from seven counties in the Ilam and Kermanshah provinces of western Iran (Ilam, Aseman abad, Holaylan, Chardavol, Dareh shahr, Gilangharb, and Sarpul). A set of 3 microsatellite primer pairs revealed a total of 75 alleles; the number of alleles varied from 15 to 34 for each marker. A high level of genetic variability was observed among A. rabiei isolates in the region. Genetic diversity was high (He = 0.788) within populations with corresponding high average gene flow and low genetic distances between populations. The smallest genetic distance was observed between isolates from Ilam and Chardavol. Both mating types were present in all populations, with the majority of the isolates belonging to Mat1-1 (64%), but within populations the proportions of each mating type were not significantly different from 50%. Results from this study will be useful in breeding for Ascochyta blight-resistant cultivars and developing necessary control measures

New evidence for habitat specific selection in Wadden Sea Zostera marina populations revealed by genome scanning using SNP and microsatellite markers

Eelgrass Zostera marina is an ecosystem-engineering species of outstanding importance for coastal soft sediment habitats that lives in widely diverging habitats. Our first goal was to detect divergent selection and habitat adaptation at the molecular genetic level; hence, we compared three pairs of permanently submerged versus intertidal populations using genome scans, a genetic marker-based approach. Three different statistical approaches for outlier identification revealed divergent selection at 6 loci among 46 markers (6 SNPs, 29 EST microsatellites and 11 anonymous microsatellites). These outlier loci were repeatedly detected in parallel habitat comparisons, suggesting the influence of habitat-specific selection. A second goal was to test the consistency of the general genome scan approach by doubling the number of gene-linked microsatellites and adding single nucleotide polymorphism (SNP) loci, a novel marker type for seagrasses, compared to a previous study. Reassuringly, results with respect to selection were consistent among most marker loci. Functionally interesting marker loci were linked to genes involved in osmoregulation and water balance, suggesting different osmotic stress, and reproductive processes (seed maturation), pointing to different life history strategies. The identified outlier loci are valuable candidates for further investigation into the genetic basis of natural selection

Simple sequence repeat variation in the Daphnia pulex genome

Background: Simple sequence repeats (SSRs) are highly variable features of all genomes. Their rapid evolution makes them useful for tracing the evolutionary history of populations and investigating patterns of selection and mutation across gnomes. The recently sequenced Daphnia pulex genome provides us with a valuable data set to study the mode and tempo of SSR evolution, without the inherent biases that accompany marker selection. Results: Here we catalogue SSR loci in the Daphnia pulex genome with repeated motif sizes of 1-100 nucleotides with a minimum of 3 perfect repeats. We then used whole genome shotgun reads to determine the average heterozygosity of each SSR type and the relationship that it has to repeat number, motif size, motif sequence, and distribution of SSR loci. We find that SSR heterozygosity is motif specific, and positively correlated with repeat number as well as motif size. For non-repeat unit polymorphisms, we identify a motif-dependent end-nucleotide polymorphism bias that may contribute to the patterns of abundance for specific homopolymers, dimers, and trimers. Our observations confirm the high frequency of multiple unit variation (multistep) at large microsatellite loci, and further show that the occurrence of multiple unit variation is dependent on both repeat number and motif size. Using the Daphnia pulex genetic map, we show a positive correlation between dimer and trimer frequency and recombination. Conclusions: This genome-wide analysis of SSR variation in Daphnia pulex indicates that several aspects of SSR variation are motif dependent and suggests that a combination of unit length variation and end repeat biased base substitution contribute to the unique spectrum of SSR repeat loci

Characterization of simple sequence repeats (SSRs) from Phlebotomus papatasi (Diptera: Psychodidae) expressed sequence tags (ESTs)

<p>Abstract</p> <p>Background</p> <p><it>Phlebotomus papatasi </it>is a natural vector of <it>Leishmania major</it>, which causes cutaneous leishmaniasis in many countries. Simple sequence repeats (SSRs), or microsatellites, are common in eukaryotic genomes and are short, repeated nucleotide sequence elements arrayed in tandem and flanked by non-repetitive regions. The enrichment methods used previously for finding new microsatellite loci in sand flies remain laborious and time consuming; <it>in silico </it>mining, which includes retrieval and screening of microsatellites from large amounts of sequence data from sequence data bases using microsatellite search tools can yield many new candidate markers.</p> <p>Results</p> <p>Simple sequence repeats (SSRs) were characterized in <it>P. papatasi </it>expressed sequence tags (ESTs) derived from a public database, National Center for Biotechnology Information (NCBI). A total of 42,784 sequences were mined, and 1,499 SSRs were identified with a frequency of 3.5% and an average density of 15.55 kb per SSR. Dinucleotide motifs were the most common SSRs, accounting for 67% followed by tri-, tetra-, and penta-nucleotide repeats, accounting for 31.1%, 1.5%, and 0.1%, respectively. The length of microsatellites varied from 5 to 16 repeats. Dinucleotide types; AG and CT have the highest frequency. Dinucleotide SSR-ESTs are relatively biased toward an excess of (AX)n repeats and a low GC base content. Forty primer pairs were designed based on motif lengths for further experimental validation.</p> <p>Conclusion</p> <p>The first large-scale survey of SSRs derived from <it>P. papatasi </it>is presented; dinucleotide SSRs identified are more frequent than other types. EST data mining is an effective strategy to identify functional microsatellites in <it>P. papatasi</it>.</p