Y-Spect: a Multi-Method Gamma Spectrometry Analysis Program

To accomplish a more accurate, precise and correct interpretation and analysis of spectrum data collecting from a gamma spectrometry counting system, a fully interactive computer code, named Y-Spect, has been developed by using the Delphi 7.0 programming language. The code combines several popular methods for peak search, i.e.: Mariscotti, Phillips-Marlow, Robertson et al., Routti-Prussin, Black, Sterlinski, Savitzky-Golay and Block et al. Any combinations of those methods can be chosen during a peak searching process, which can be performed in automatic or manual mode. Moving Window Average- and Savitzky-Golay-methods are available for spectrum data smoothing. Peak fitting is done by using a non-linear least square method of Levenberg-Marquardt for either a pure Gaussian peak shape or one with an additional Right/Left Tail function. Other than standard features, such as: peak identification and determination of: continuum, region of interest (ROI), and peak area, etc., Y-Spect has also a special feature which can predict the existence of escape- and/or sum peaks that belong to a certain radioisotope. Aside from displaying the complete spectrum graph, including: singlet or multiplet ROIs and peak identifications, Y-Spect can also display the first- or second-derivative of the spectrum data. Data evaluation is given as: isotope names, peak energy, Net-Count(-Rate), etc. Y-Spect is provided with a complete ENDF/B-VII.0 gamma-ray library file that contains of 16089 gamma energy lines from 1420 different radioisotopes. Other general specifications are: maximum number of: spectrum\u27s channels = 16*1024; ROIs = 2*1024; ROI\u27s width = 2*1024 channels; Overlapping peaks (multiplet) = 20; Identified isotopes = 3*1024, and Isotope library\u27s energy lines = 16*1024.Received: 16 January 2013; Revised: 21 April 2013; Accepted: 25 April 201

A cartoon illustrating the method used here for identifying post-macaque SDs based on chromosomal synteny

Using the liftOver tool [] from the UCSC genome browser group, a pair of human SDs (A and B) is mapped to the same location (A') in the macaque genome. A and B (large block) are thus considered the product of an SD event that occurred after the split of human from macaque lineages. Then 1 kb sequences (small block) adjacent to A or B were aligned to the macaque genome. If only the sequence next to A was mapped next to A', then A is designated as the parental copy and B as the derivative.<p><b>Copyright information:</b></p><p>Taken from "Asymmetric histone modifications between the original and derived loci of human segmental duplications"</p><p>http://genomebiology.com/2008/9/7/R105</p><p>Genome Biology 2008;9(7):R105-R105.</p><p>Published online 3 Jul 2008</p><p>PMCID:PMC2530858.</p><p></p

Pattern of histone modifications for the four SD pairs in Figure 3, ordered left to right to match their order from top to bottom in Figure 3

Each point represents the number of ChIP-Seq tags in a 5 kb genomic region, with red for parental and blue for derivative SDs. Horizontal axes are the position relative to the 5' end of a parental locus. Data for a derivative region is ordered with respect to its parent.<p><b>Copyright information:</b></p><p>Taken from "Asymmetric histone modifications between the original and derived loci of human segmental duplications"</p><p>http://genomebiology.com/2008/9/7/R105</p><p>Genome Biology 2008;9(7):R105-R105.</p><p>Published online 3 Jul 2008</p><p>PMCID:PMC2530858.</p><p></p

Gene and pseudogene annotations in four pairs of human SDs with known duplication directions

The parental locus of each pair is depicted first, followed immediately by its derivative.<p><b>Copyright information:</b></p><p>Taken from "Asymmetric histone modifications between the original and derived loci of human segmental duplications"</p><p>http://genomebiology.com/2008/9/7/R105</p><p>Genome Biology 2008;9(7):R105-R105.</p><p>Published online 3 Jul 2008</p><p>PMCID:PMC2530858.</p><p></p

Additional file 1: Figures S1–S15. of Characteristics of allelic gene expression in human brain cells from single-cell RNA-seq data analysis

Figure S1. SNP calling result using mouse embryonic scRNA-seq data. Figure S2. A cartoon illustrating the steps and criteria in our allelic expression. Figure S3. Numbers of hetSNP called for the six human brains. Figure S4. The effect of cell numbers on hetSNP calling and the genomic distribution of hetSNPs. Figure S5. Boxplots showing the numbers of brain cells expressing reference (R) or alternative (A) alleles (allelic read depth ≥ 2). Figure S6. Boxplots showing the percentages of reference reads (vs total reads) at hetSNP sites in brain cells (read depth for each of the alleles was ≥2 and the sum of read depths was ≥10). Figure S7. Allelic expression of hetSNPs within human imprinted genes in brain cells. Figure S8. Allelic expression of hetSNPs within mouse imprinted genes in embryonic cells. Figure S9. Numbers of hetSNPs sites with different reference allele ratios. Figure S10. Numbers of hetSNPs sites with different reference allele ratios, after scRNA-seq reads from cells of the same type in individual brains were pooled. Figure S11. Statistical summaries of allelic expression at the gene level. Figure S12. FPKM cutoff values for defining the top 30 percentile of genes in each cell. Figure S13. Monoallelic expression in subsampled neurons. Figure S14. Numbers of individual cells in which a MA gene was detected. Figure S15. Comparison of monoallelic expression between neurons and astrocytes in adult37, adult47 and adult50. (PDF 2190 kb

Additional file 2: Tables S1, S4 and S5. of Characteristics of allelic gene expression in human brain cells from single-cell RNA-seq data analysis

Table S1. Cell numbers used for scRNA-seq of the brains. This table is based on the cell classification in the original study (Darmanis et al., 2015). The column of “Experiment_sample_name” lists the sample labels in the original research. Only the first six adult samples were used in our analysis. Table S4. List of disease-related genes showing monoallelic expression in human brains at the cell-type level. Table S5. List of module genes from WGCNA. Gene symbols of three significant modules (salmon2, salmon4 and magenta) were listed. (DOC 68 kb

Additional file 3: Table S2. of Characteristics of allelic gene expression in human brain cells from single-cell RNA-seq data analysis

Gene biased status in each cell of individual brains. The three numbers of SNPs supporting allele bias (MA/BA/Unknown) and the letter indicating gene bias status (M: MA; B: BA; U: Unknown) were separated by slash (/). A dot (.) means data not available. (TXT 5965 kb

Characterization of Human Pseudogene-Derived Non-Coding RNAs for Functional Potential

<div><p>Thousands of pseudogenes exist in the human genome and many are transcribed, but their functional potential remains elusive and understudied. To explore these issues systematically, we first developed a computational pipeline to identify transcribed pseudogenes from RNA-Seq data. Applying the pipeline to datasets from 16 distinct normal human tissues identified ∼3,000 pseudogenes that could produce non-coding RNAs in a manner of low abundance but high tissue specificity under normal physiological conditions. Cross-tissue comparison revealed that the transcriptional profiles of pseudogenes and their parent genes showed mostly positive correlations, suggesting that pseudogene transcription could have a positive effect on the expression of their parent genes, perhaps by functioning as competing endogenous RNAs (ceRNAs), as previously suggested and demonstrated with the <i>PTEN</i> pseudogene, <i>PTENP1</i>. Our analysis of the ENCODE project data also found many transcriptionally active pseudogenes in the GM12878 and K562 cell lines; moreover, it showed that many human pseudogenes produced small RNAs (sRNAs) and some pseudogene-derived sRNAs, especially those from antisense strands, exhibited evidence of interfering with gene expression. Further integrated analysis of transcriptomics and epigenomics data, however, demonstrated that trimethylation of histone 3 at lysine 9 (H3K9me3), a posttranslational modification typically associated with gene repression and heterochromatin, was enriched at many transcribed pseudogenes in a transcription-level dependent manner in the two cell lines. The H3K9me3 enrichment was more prominent in pseudogenes that produced sRNAs at pseudogene loci and their adjacent regions, an observation further supported by the co-enrichment of SETDB1 (a H3K9 methyltransferase), suggesting that pseudogene sRNAs may have a role in regional chromatin repression. Taken together, our comprehensive and systematic characterization of pseudogene transcription uncovers a complex picture of how pseudogene ncRNAs could influence gene and pseudogene expression, at both epigenetic and post-transcriptional levels.</p></div

Mammalian TBX1 Preferentially Binds and Regulates Downstream Targets Via a Tandem T-site Repeat

<div><p>Haploinsufficiency or mutation of <i>TBX1</i> is largely responsible for the etiology of physical malformations in individuals with velo-cardio-facial/DiGeorge syndrome (VCFS/DGS/22q11.2 deletion syndrome). <i>TBX1</i> encodes a transcription factor protein that contains an evolutionarily conserved DNA binding domain termed the T-box that is shared with other family members. All T-box proteins, examined so far, bind to similar but not identical consensus DNA sequences, indicating that they have specific binding preferences. To identify the TBX1 specific consensus sequence, Systematic Evolution of Ligands by Exponential Enrichment (SELEX) was performed. In contrast to other TBX family members recognizing palindrome sequences, we found that TBX1 preferentially binds to a tandem repeat of 5′-AGGTGTGAAGGTGTGA-3′. We also identified a second consensus sequence comprised of a tandem repeat with a degenerated downstream site. We show that three known human disease-causing <i>TBX1</i> missense mutations (F148Y, H194Q and G310S) do not alter nuclear localization, or disrupt binding to the tandem repeat consensus sequences, but they reduce transcriptional activity in cell culture reporter assays. To identify <i>Tbx1</i>-downstream genes, we performed an <i>in silico</i> genome wide analysis of potential <i>cis</i>-acting elements in DNA and found strong enrichment of genes required for developmental processes and transcriptional regulation. We found that TBX1 binds to 19 different loci <i>in vitro</i>, which may correspond to putative <i>cis</i>-acting binding sites. <i>In situ</i> hybridization coupled with luciferase gene reporter assays on three gene loci, <i>Fgf8, Bmper, Otog-MyoD</i>, show that these motifs are directly regulated by TBX1 <i>in vitro</i>. Collectively, the present studies establish new insights into molecular aspects of TBX1 binding to DNA. This work lays the groundwork for future <i>in vivo</i> studies, including chromatin immunoprecipitation followed by next generation sequencing (ChIP-Seq) to further elucidate the molecular pathogenesis of VCFS/DGS.</p></div

SELEX-Selection of specific oligonucleotides bound to GST-TBX1.

<p>A: A pipeline illustrating the SELEX method is shown. The dsDNA was generated by PCR of the selected oligonucleotides at each found and incubated with GST-TBX1. A total of 6 rounds of selection was performed. B: EMSA was used to detect specific GST-TBX1 and [α-32P]dCTP labeled DNA complexes at 0, 2, 4 and 6 rounds of selection, with or without cold competitor (R6, cold PCR products from round 6; T, ds DNA harboring the published Brachyury half site). C: Sequence alignment shows that the optimal DNA binding motif for TBX1 is AGGTGT(G/T)(A/T) followed by two repeated similar motifs termed the Tandem Repeat (TR) and Half Site Partial Site as shown (½SPS). D: Distribution of sequences with different consensus binding motifs within the pool of oligonucleotides after 6 rounds of selection (total number  = 60).</p