Discovery Of Hotspots Research Articles

Discovery of a stable expression hot spot in the genome of Chinese hamster ovary cells using lentivirus-based random integration

The conventional method for construction of stable expression cell lines is mainly based on random integration. However, one drawback of random integration is that the target gene might be integrated into a heterochromatin region or an unstable region of chromatin, thus requiring multiple rounds of selection to obtain desirable expressing cell lines. Rational cell line construction can overcome this shortcoming by integrating transgenes specifically into a stable hot spot within the genome. As such, the discovery of novel effective hot spots becomes critical for this new method of cell line construction. Here we report a practical method for discovery of new stable hot spots through random integration of lentivirus. We describe the thorough study of a hot spot located at NW_006880285.1. The expression stability of this hot spot was verified by detecting Zsgreen1 reporter gene expression for over 50 passages. When cells were adapted to suspension culture, they continuously expressed the Zsgreen1 reporter gene. In addition, this cell suspension was able to stably express the reporter gene for an additional 50 passages. Another finding was that cells with the NGGH gene inserted into the same hot spot were also able to stably express respective protein over 50 passages. In summary, this research offers an easy and new method for researchers to identify stable hot spots within the Chinese Hamster Ovary (CHO) genome on their own, thus contributing to the development of site-specific integration studies in the future.

Research Hotspot Prediction and Regular Evolutionary Pattern Identification Based on NSFC Grants Using NMF and Semantic Retrieval

Analyzing the research hotspots and regular evolutionary patterns of R&D projects can help researchers find potential information. This paper considers the titles of the National Natural Science Foundation of China (NSFC) grants that have been awarded in the past 20 years as the research objects. First, we analyze the number and funding amounts of project grants for each department over the past 20 years. Second, we propose a topic discovery method that is based on nonnegative matrix factorization and further propose a keyword scoring method that is based on semantic retrieval, from which we can discover the regular evolutionary patterns of research hotspots. Finally, we conduct experiments on datasets on grants in the Department of Information Science. To identify research characteristics, trends and prospects, we explore the regular evolutionary patterns of research hotspots via experiments using three methods from multiple perspectives: word cloud, topic corresponded to keywords display and topic evolution display. The results of the experimental studies demonstrate that researchers can keep abreast of the main content and hotspots in the research field via hot spot discovery and the identification of regular evolutionary pattern of NSFC grants. This study can also help the government improve the allocation of scientific and technological resources and help decision makers make scientific decisions.

Identification of Local Clusters of Mutation Hotspots in Cancer-Related Genes and Their Biological Relevance.

Mutation hotspots are either solitary amino acid residues or stretches of amino acids that show elevated mutation frequency in cancer-related genes, but their prevalence and biological relevance are not completely understood. Here, we developed a Smith-Waterman algorithm-based mutation hotspot discovery method, MutClustSW, to identify mutation hotspots of either single or clustered amino acid residues. We identified 181 missense mutation hotspots from COSMIC and TCGA mutation databases. In addition to 77 single amino acid residue hotspots (42.5 percent) including well-known mutation hotspots such as IDH1 (p.R132) and BRAF (p.V600), we identified 104 mutation hotspots (57.5 percent) as clusters or stretches of multiple amino acids, and the hotspots on MUC2, EPPK1, KMT2C, and TP53 were larger than 50 amino acids. Twelve of 27 nonsense mutation hotspots (44.4 percent) were observed in four cancer-related genes, TP53, ARID1A, CDKN2A, and PTEN, suggesting that truncating mutations on some tumor suppressor genes are not randomly distributed as previously assumed. We also show that hotspot mutations have higher mutation allele frequency than non-hotspots, and the hotspot information can be used to prioritize the cancer drivers. Together, the proposed algorithm and the mutation hotspot information can serve as valuable resources in the selection of functional driver mutations and associated genes.

Accelerating Discovery of Functional Mutant Alleles in Cancer.

Most mutations in cancer are rare, which complicates the identification of therapeutically significant mutations and thus limits the clinical impact of genomic profiling in patients with cancer. Here, we analyzed 24,592 cancers including 10,336 prospectively sequenced patients with advanced disease to identify mutant residues arising more frequently than expected in the absence of selection. We identified 1,165 statistically significant hotspot mutations of which 80% arose in 1 in 1,000 or fewer patients. Of 55 recurrent in-frame indels, we validated that novel AKT1 duplications induced pathway hyperactivation and conferred AKT inhibitor sensitivity. Cancer genes exhibit different rates of hotspot discovery with increasing sample size, with few approaching saturation. Consequently, 26% of all hotspots in therapeutically actionable oncogenes were novel. Upon matching a subset of affected patients directly to molecularly targeted therapy, we observed radiographic and clinical responses. Population-scale mutant allele discovery illustrates how the identification of driver mutations in cancer is far from complete.Significance: Our systematic computational, experimental, and clinical analysis of hotspot mutations in approximately 25,000 human cancers demonstrates that the long right tail of biologically and therapeutically significant mutant alleles is still incompletely characterized. Sharing prospective genomic data will accelerate hotspot identification, thereby expanding the reach of precision oncology in patients with cancer. Cancer Discov; 8(2); 174-83. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 127.

Research on Hotspot Discovery of Academic Resources based on Hadoop

In view of the situation that user can not access timely and accurately to the current research hotspot, this paper proposes a hotspot of academic resources discovery method, which aims to combine the map-reduce function of Hadoop with the text clustering technology based on DBSCAN, and to realize hotspot discovery of academic resources in distributed environment. By comparing the efficiency of hotspot discovery of academic resources in the single centralized architecture management environment, the efficiency and rationality of Hadoop-based hotspot discovery of academic resources is proved.

Understanding G Protein-Coupled Receptor Allostery via Molecular Dynamics Simulations: Implications for Drug Discovery.

Unraveling the mystery of protein allostery has been one of the greatest challenges in both structural and computational biology. However, recent advances in computational methods, particularly molecular dynamics (MD) simulations, have led to its utility as a powerful and popular tool for the study of protein allostery. By capturing the motions of a protein's constituent atoms, simulations can enable the discovery of allosteric hot spots and the determination of the mechanistic basis for allostery. These structural and dynamic studies can provide a foundation for a wide range of applications, including rational drug design and protein engineering. In our laboratory, the use of MD simulations and network analysis assisted in the elucidation of the allosteric hotspots and intracellular signal transduction of G protein-coupled receptors (GPCRs), primarily on one of the adenosine receptor subtypes, A2A adenosine receptor (A2AAR). In this chapter, we describe a method for calculating the map of allosteric signal flow in different GPCR conformational states and illustrate how these concepts have been utilized in understanding the mechanism of GPCR allostery. These structural studies will provide valuable insights into the allosteric and orthosteric modulations that would be of great help to design novel drugs targeting GPCRs in pathological states.

Effects of Demographic History on the Detection of Recombination Hotspots from Linkage Disequilibrium.

In some species, meiotic recombination is concentrated in small genomic regions. These “recombination hotspots” leave signatures in fine-scale patterns of linkage disequilibrium, raising the prospect that the genomic landscape of hotspots can be characterized from sequence variation. This approach has led to the inference that hotspots evolve rapidly in some species, but are conserved in others. Historic demographic events, such as population bottlenecks, are known to affect patterns of linkage disequilibrium across the genome, violating population genetic assumptions of this approach. Although such events are prevalent, demographic history is generally ignored when making inferences about the evolution of recombination hotspots. To determine the effect of demography on the detection of recombination hotspots, we use the coalescent to simulate haplotypes with a known recombination landscape. We measure the ability of popular linkage disequilibrium-based programs to detect hotspots across a range of demographic histories, including population bottlenecks, hidden population structure, population expansions, and population contractions. We find that demographic events have the potential to greatly reduce the power and increase the false positive rate of hotspot discovery. Neither the power nor the false positive rate of hotspot detection can be predicted without also knowing the demographic history of the sample. Our results suggest that ignoring demographic history likely overestimates the power to detect hotspots and therefore underestimates the degree of hotspot sharing between species. We suggest strategies for incorporating demographic history into population genetic inferences about recombination hotspots.

Significant Linear Hotspot Discovery

Given a spatial network and a collection of activities (e.g., pedestrian fatality reports, crime reports), Significant Linear Hotspot Discovery (SLHD) finds all shortest paths in the spatial network where the concentration of activities is statistically significantly high. SLHD is important for societal applications in transportation safety or public safety such as finding paths with significant concentrations of accidents or crimes. SLHD is challenging because 1) there are a potentially large number of candidate paths ( $\sim10^{16}$ ) in a given dataset with millions of activities and road network nodes and 2) test statistic (e.g., density ratio) is not monotonic. Hotspot detection approaches on euclidean space (e.g., SaTScan) may miss significant paths since a large fraction of an area bounded by shapes in euclidean space for activities on a path will be empty. Previous network-based approaches consider only paths between road intersections but not activities. This paper proposes novel models and algorithms for discovering statistically significant linear hotspots using the algorithms of neighbor node filter, shortest path tree pruning, and Monte Carlo speedup. We present case studies comparing the proposed approaches with existing techniques on real data. Experimental results show that the proposed algorithms yield substantial computational savings without reducing result quality.

A patent hotspot discovery method

Due to the importance of patent, many studies have been done in patent analysis. However, the problem of finding the hotspots of competitors is seldom considered. Although there exist some hotspot discovery methods in Micro-blog and online public opinion, it is difficult to be directly applied because of the particularity of the patent text. In this paper, we proposed a text-clustering-based patent hotspot discovery method to find the hotspots of competitors. We first measure the similarity between patents by both semantic association and IPC association. After that, we use a hierarchical clustering algorithm to find the research topics and name for them. Then, we calculate the hotness of the technical phrases in order to find the hotspots. Finally, we use a case study of Huawei company to show the effectiveness of the proposed method.

Computational Systems Biology Approach Predicts Regulators and Targets of microRNAs and Their Genomic Hotspots in Apoptosis Process.

Novel computational systems biology tools such as common targets analysis, common regulators analysis, pathway discovery, and transcriptomic-based hotspot discovery provide new opportunities in understanding of apoptosis molecular mechanisms. In this study, after measuring the global contribution of microRNAs in the course of apoptosis by Affymetrix platform, systems biology tools were utilized to obtain a comprehensive view on the role of microRNAs in apoptosis process. Network analysis and pathway discovery highlighted the crosstalk between transcription factors and microRNAs in apoptosis. Within the transcription factors, PRDM1 showed the highest upregulation during the course of apoptosis, with more than 9-fold expression increase compared to non-apoptotic condition. Within the microRNAs, MIR1208 showed the highest expression in non-apoptotic condition and downregulated by more than 6 fold during apoptosis. Common regulators algorithm showed that TNF receptor is the key upstream regulator with a high number of regulatory interactions with the differentially expressed microRNAs. BCL2 and AKT1 were the key downstream targets of differentially expressed microRNAs. Enrichment analysis of the genomic locations of differentially expressed microRNAs led us to the discovery of chromosome bands which were highly enriched (p<0.01) with the apoptosis-related microRNAs, such as 13q31.3, 19p13.13, and Xq27.3 This study opens a new avenue in understanding regulatory mechanisms and downstream functions in the course of apoptosis as well as distinguishing genomic-enriched hotspots for apoptosis process.

Abstract 4263: Identifying somatic mutation hotspots across protein family alignments

Abstract In cancer, somatic driver mutations often target “hotspots” of paralogous residues across evolutionarily related members of a single protein family. These hotspots usually confer a dominant oncogenic function, such as constitutive catalytic activity or binding. These hotspots generally represent the most “druggable” genetic alterations. Examples of protein families targeted in such a manner include the Ras and receptor Tyrosine Kinase family. Protein family hotspots are not directly assessed by standard computational approaches for statistically nominating genes under positive somatic selection, such as MutSig, InVex, or MuSic. These methods only assess single genes for statistical enrichment of total mutation burden or the presence of mutation hotspots. As a result, such approaches are only powered to detect protein family hotspots in cases where each (or at least one) protein family member is very frequently mutated (e.g. KRAS, NRAS, HRAS). They are not powered to detect hotspots in highly functionally redundant families, of which each member may be only mutated in &amp;lt;&amp;lt;1% of a tumor type. To directly identify such protein-family hotspots, we have developed a computational framework, MutPfam. This framework superimposes mutation data onto Pfam (http://pfam.sanger.ac.uk/) (protein family and subfamily multiple sequence alignments and identifies statistically significant hotspots. We demonstate our method on protein families involved in the catalysis of small-molecular catabolism using the TCGA Pan-Cancer dataset (https://tcga-data.nci.nih.gov/) comprising somatic mutation calls for 4608 whole-exome sequenced patients and 21 tumor types. As we demonstrate, our framework enables the de novo discovery of mutation hotspots across protein families. Though developed for somatic mutation analysis, this framework could also be applied to the analysis of inherited mutations. Computational issues meriting future investigation include (1) alternate definitions of protein alignments and sub-families (e.g. involving three dimensional structure, biophysical constraints) (2) improved statistical modeling of the local background mutation rate (incorporating local genomic covariates that mediate heterogeneity) (3) incorporation of variant functional impact scores for missense variants. Citation Format: Marcin Imielinski, Charles Du, Matthew Meyerson. Identifying somatic mutation hotspots across protein family alignments. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4263. doi:10.1158/1538-7445.AM2014-4263

Mapping family connectedness across space and time

Understanding the structure and evolution of family networks embedded in space and time is crucial for various fields such as disaster evacuation planning and provision of care to the elderly. Computation and visualization can potentially play a key role in analyzing and understanding such networks. Graph visualization methods are effective in discovering network patterns; however, they have inadequate capability in discovering spatial and temporal patterns of connections in a network especially when the network exists and changes across space and time. We introduce a measure of family connectedness that summarizes the dynamic relationships in a family network by taking into account the distance (how far individuals live apart), time (the duration of individuals’ coexistence within a neighborhood), and the relationship (kinship or kin proximity) between each pair of individuals. By mapping the family connectedness over a series of time intervals, the method facilitates the discovery of hot spots (hubs) where family connectedness is strong and the changing patterns of such spots across space and time. We demonstrate our approach using a data set of nine families from the US North. Our results highlight that family connectedness reflects changing demographic processes such as migration and population growth.

Abstract A22: Identifying somatic mutation hotspots across protein family alignments.

Abstract In cancer, somatic driver mutations often target “hotspots” of paralogous residues across evolutionarily related members of a single protein family. Mutation hotspots usually confer a dominant oncogenic function and are among the most druggable cancer sequence alterations (e.g. EGFR L858R, BRAF V600E). Examples of protein families targeted in such a manner include the Ras and receptor tyrosine kinase families (1). Protein family hotspots are not directly assessed by standard computational approaches for statistically nominating genes under positive somatic selection in cancer, such as MutSig (2), InVex (3), or MuSic (4). These methods only assess single genes for statistical enrichment of total mutation burden or the presence of mutation hotspots, and thus are only powered to detect protein family hotspots in cases where each (or at least one) protein family member is very frequently mutated (e.g. KRAS, NRAS, HRAS). In particular, they are not powered to detect hotspots in highly functionally redundant families, of which each member may be only mutated in &amp;lt;&amp;lt;1% of cases. To directly identify protein-family hotspots, we have developed a computational framework, MutPfam. This framework superimposes mutation data onto Pfam (http://pfam.sanger.ac.uk/) protein family and subfamily multiple sequence alignments and identifies statistically significant hotspots. We demonstrate our method on the Ras Pfam family (PF00071.17) using TCGA Pan-Cancer dataset (https://tcga-data.nci.nih.gov/) comprising somatic mutation calls for 4608 whole-exome sequenced patients and 21 tumor types. Using phylogeny analysis of this 134-member protein family, we analyzed 110 subfamilies for the presence of significantly mutated hotspots. In addition to canonical KRAS, NRAS, and HRAS hotspots in the switch I (e.g. KRAS G12V, KRAS G13D) and switch II (e.g. HRAS Q61R) domains, we identified several other hotspots achieving significance following Bonferonni correction. Among our top hits was a recently identified melanoma hotspot in RAC1 (3), previously uncharacterized paralogues of a known hotspot in RHOA (RHOA E40Q, RALA D49G, RND1 E48K), and a novel recurrently mutated position in the RAB subfamily (e.g. RAB10 R28C, RAB11B R30H, RAB17 R38W). As we demonstrate, our framework enables the de novo discovery of mutation hotspots across protein families, including those that are rarely mutated in any single family member. Computational issues meriting future investigation include (1) alternate definitions of protein alignments and sub-families (e.g. involving three dimensional structure, biophysical constraints) (2) improved statistical modeling of the local background mutation rate (3) incorporation of variant functional impact scores for missense variants. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A22. Citation Format: Marcin Imielinski, Charles Du, Matthew Meyerson. Identifying somatic mutation hotspots across protein family alignments. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A22.

Public Opinion Hotspot Discovery Algorithm Based on Fuzzy Clustering LDA

The discovery of public opinion hotspot is an important aspect of public opinion research, and because many similarities and relevance exist between hot topics, we propose a hot topic clustering algorithm to find the hotspot in public opinions. Since fuzzy set can handle non-precision data well, the fuzzy algorithm can reduce the influences of the uncertainty of public opinion data. Based on LDA topic extraction we cluster the topical words by fuzzy method, and take the topic probability as word membership to the cluster. It can reduce the noise data and improve the ability of hotspot discovery that aggregate the similar and related topic to one class. The topical key words with high probability in cluster are the hotspot, and singular cluster with few words can be looked as outlier. The algorithm is demonstrated by example analysis in detail.

Research on Hotspot Discovery in Internet Public Opinions Based on Improved -Means

How to discover hotspot in the Internet public opinions effectively is a hot research field for the researchers related which plays a key role for governments and corporations to find useful information from mass data in the Internet. An improved K-means algorithm for hotspot discovery in internet public opinions is presented based on the analysis of existing defects and calculation principle of original K-means algorithm. First, some new methods are designed to preprocess website texts, select and express the characteristics of website texts, and define the similarity between two website texts, respectively. Second, clustering principle and the method of initial classification centers selection are analyzed and improved in order to overcome the limitations of original K-means algorithm. Finally, the experimental results verify that the improved algorithm can improve the clustering stability and classification accuracy of hotspot discovery in internet public opinions when used in practice.

Sequence- and Interactome-Based Prediction of Viral Protein Hotspots Targeting Host Proteins: A Case Study for HIV Nef

Virus proteins alter protein pathways of the host toward the synthesis of viral particles by breaking and making edges via binding to host proteins. In this study, we developed a computational approach to predict viral sequence hotspots for binding to host proteins based on sequences of viral and host proteins and literature-curated virus-host protein interactome data. We use a motif discovery algorithm repeatedly on collections of sequences of viral proteins and immediate binding partners of their host targets and choose only those motifs that are conserved on viral sequences and highly statistically enriched among binding partners of virus protein targeted host proteins. Our results match experimental data on binding sites of Nef to host proteins such as MAPK1, VAV1, LCK, HCK, HLA-A, CD4, FYN, and GNB2L1 with high statistical significance but is a poor predictor of Nef binding sites on highly flexible, hoop-like regions. Predicted hotspots recapture CD8 cell epitopes of HIV Nef highlighting their importance in modulating virus-host interactions. Host proteins potentially targeted or outcompeted by Nef appear crowding the T cell receptor, natural killer cell mediated cytotoxicity, and neurotrophin signaling pathways. Scanning of HIV Nef motifs on multiple alignments of hepatitis C protein NS5A produces results consistent with literature, indicating the potential value of the hotspot discovery in advancing our understanding of virus-host crosstalk.

Large-scale discovery of insertion hotspots and preferential integration sites of human transposed elements

Throughout evolution, eukaryotic genomes have been invaded by transposable elements (TEs). Little is known about the factors leading to genomic proliferation of TEs, their preferred integration sites and the molecular mechanisms underlying their insertion. We analyzed hundreds of thousands nested TEs in the human genome, i.e. insertions of TEs into existing ones. We first discovered that most TEs insert within specific ‘hotspots’ along the targeted TE. In particular, retrotransposed Alu elements contain a non-canonical single nucleotide hotspot for insertion of other Alu sequences. We next devised a method for identification of integration sequence motifs of inserted TEs that are conserved within the targeted TEs. This method revealed novel sequences motifs characterizing insertions of various important TE families: Alu, hAT, ERV1 and MaLR. Finally, we performed a global assessment to determine the extent to which young TEs tend to nest within older transposed elements and identified a 4-fold higher tendency of TEs to insert into existing TEs than to insert within non-TE intergenic regions. Our analysis demonstrates that TEs are highly biased to insert within certain TEs, in specific orientations and within specific targeted TE positions. TE nesting events also reveal new characteristics of the molecular mechanisms underlying transposition.

Discovery of feature-based hot spots using supervised clustering

Feature-based hot spots are localized regions where the attributes of objects attain high values. There is considerable interest in automatic identification of feature-based hot spots. This paper approaches the problem of finding feature-based hot spots from a data mining perspective, and describes a method that relies on supervised clustering to produce a list of hot spot regions. Supervised clustering uses a fitness function rewarding isolation of the hot spots to optimally subdivide the dataset. The clusters in the optimal division are ranked using the interestingness of clusters that encapsulate their utility for being hot spots. Hot spots are associated with the top ranked clusters. The effectiveness of supervised clustering as a hot spot identification method is evaluated for four conceptually different clustering algorithms using a dataset describing the spatial distribution of ground ice on Mars. Clustering solutions are visualized by specially developed raster approximations. Further assessment of the ability of different algorithms to yield hot spots is performed using raster approximations. Density-based clustering algorithm is found to be the most effective for hot spot identification. The results of the hot spot discovery by supervised clustering are comparable to those obtained using the G * statistic, but the new method offers a high degree of automation, making it an ideal tool for mining large datasets for the existence of potential hot spots.

Recombination rate estimation in the presence of hotspots

Fine-scale estimation of recombination rates remains a challenging problem. Experimental techniques can provide accurate estimates at fine scales but are technically challenging and cannot be applied on a genome-wide scale. An alternative source of information comes from patterns of genetic variation. Several statistical methods have been developed to estimate recombination rates from randomly sampled chromosomes. However, most such methods either make poor assumptions about recombination rate variation, or simply assume that there is no rate variation. Since the discovery of recombination hotspots, it is clear that recombination rates can vary over many orders of magnitude at the fine scale. We present a method for the estimation of recombination rates in the presence of recombination hotspots. We demonstrate that the method is able to detect and accurately quantify recombination rate heterogeneity, and is a substantial improvement over a commonly used method. We then use the method to reanalyze genetic variation data from the HLA and MS32 regions of the human genome and demonstrate that the method is able to provide accurate rate estimates and simultaneously detect hotspots.

Recombination between Palindromes P5 and P1 on the Human Y Chromosome Causes Massive Deletions and Spermatogenic Failure

It is widely believed that at least three nonoverlapping regions of the human Y chromosome-AZFa, AZFb, and AZFc ("azoospermia factors" a, b, and c)-are essential for normal spermatogenesis. These intervals are defined by interstitial Y-chromosome deletions that impair or extinguish spermatogenesis. Deletion breakpoints, mechanisms, and lengths, as well as inventories of affected genes, have been elucidated for deletions of AZFa and of AZFc but not for deletions of AZFb or of AZFb plus AZFc. We studied three deletions of AZFb and eight deletions of AZFb plus AZFc, as assayed by the STSs defining these intervals. Guided by Y-chromosome sequence, we localized breakpoints precisely and were able to sequence nine of the deletion junctions. Homologous recombination can explain seven of these deletions but not the remaining two. This fact and our discovery of breakpoint hotspots suggest that factors in addition to homology underlie these deletions. The deletions previously thought to define AZFb were found to extend from palindrome P5 to the proximal arm of palindrome P1, 1.5 Mb within AZFc. Thus, they do not define a genomic region separate from AZFc. We also found that the deletions of AZFb plus AZFc, as assayed by standard STSs heretofore available, in fact extend from P5 to the distal arm of P1 and spare distal AZFc. Both classes of deletions are massive: P5/proximal-P1 deletions encompass up to 6.2 Mb and remove 32 genes and transcripts; P5/distal-P1 deletions encompass up to 7.7 Mb and remove 42 genes and transcripts. To our knowledge, these are the largest of all human interstitial deletions for which deletion junctions and complete intervening sequence are available. The restriction of the associated phenotype to spermatogenic failure indicates the remarkable functional specialization of the affected regions of the Y chromosome.