Some of my cleverer users are using the pstools suite to shutdown each others computers. I now know how to prevent them from doing so but is it possible that the pskill commands are being logged somewhere? While I know they're doing it I don't know exactly who it is.
Check the Event Viewer on a machine that you suspect was shutdown remotely. You should be able to find a log of the Shutdown command being given and it might have the user who issued the command. You may need to tweak some logging to get that (I know Server 2k3 logs WHO sent the command, not sure about XP).
Pstools 2.2 Download
Download 🔥 https://urlca.com/2yGB8p 🔥
There have been many posts on this site either requesting simple examples illustrating the pstool package, or providing (often not-simple) ones. I've tried all of them and none of them compile for me. I've also tried to read pstools.sty, which came with my installation, but unfortunately it contained no simple examples. It seems that the following MWE is about as simple as one could possibly get.
There's talk on the web about a file called pstool-statusfile.txt, which I don't understand, but I've touch'ed it, just in case, it's zero size before and after compilation. The file trial.eps was downloaded from
When running your MWE the log complained of missing packages so I added tikz and its calc library then it worked as expected. trial.eps here is a copy of example-image.eps that is in tex distributions (if I left it as example-image ot ran without error but found the png file rather than eps so coukd not do the replacement
Characterizing cancer genomes at the chromosome-scale with high resolution is critical for advancing personalized diagnosis and disease management. However, current short-read sequencing and analytical tools are not designed for cancer genomes and are insufficient for chromosome-scale analysis of structural variant (SV) landscapes. Recent work by Shilpa Garg at the Technical University of Denmark and the University of Copenhagen combined high-resolution sequencing techniques and a novel computational platform to accurately and precisely reconstruct cancer genomes.
Garg applied long and accurate PacBio HiFi sequencing and long-range Arima Hi-C data to melanoma COLO829 cancer cell lines to examine cancerous mutations at base and haplotype resolution. These data were then processed through a novel, high throughput graph-based computational tool, pstools. Garg demonstrates that combining these approaches enables precise characterization of SV landscapes of cancer genomes, outperforming many existing methods.
With the pstools approach, the author delineates several characteristics of the cancer genome that are often missed with current analytical tools such as HiCanu, Flye, trio-hifiasm, and salsa2. Importantly, pstools is fast and accurate, enabling high-throughput and routine analyses of fully phased sequences at the chromosome scale.
Shilpa Garg leveraged 3D genomic data generated using the Arima genome-wide HiC kit to obtain Hi-C reads that were then analyzed using pstools. Published in Nature Communications, this workflow provides a foundation for streamlining cancer genome research, with important implications for improving personalized cancer diagnosis and treatment.
In the Nature Communications article, Garg details novel sequencing and computational protocols for comprehensive cancer genome reconstruction. While highly promising, the author acknowledges that pstools is limited in its ability to characterize somatic genetic variation at low variant allele frequencies, requiring future studies that explore single-cell, long=read approaches.
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Cancer genomes are highly complex and heterogeneous. The standard short-read sequencing and analytical methods are unable to provide the complete and precise base-level structural variant landscape of cancer genomes. In this work, we apply high-resolution long accurate HiFi and long-range Hi-C sequencing to the melanoma COLO829 cancer line. Also, we develop an efficient graph-based approach that processes these data types for chromosome-scale haplotype-resolved reconstruction to characterise the cancer precise structural variant landscape. Our method produces high-quality phased scaffolds on the chromosome level on three healthy samples and the COLO829 cancer line in less than half a day even in the absence of trio information, outperforming existing state-of-the-art methods. In the COLO829 cancer cell line, here we show that our method identifies and characterises precise somatic structural variant calls in important repeat elements that were missed in short-read-based call sets. Our method also finds the precise chromosome-level structural variant (germline and somatic) landscape with 19,956 insertions, 14,846 deletions, 421 duplications, 52 inversions and 498 translocations at the base resolution. Our simple pstools approach should facilitate better personalised diagnosis and disease management, including predicting therapeutic responses.
The Hi-C information in the graph (specifically along each green and red path) is helpful for disentangling the chromosomes. Right: The starting regions of chromosome (chr6 and chr1) arms occur in different components. The phasing information for Hi-C (connecting alleles in bubbles) is useful for accurately connecting the starting regions of chromosome arms.
a Whole-genome precise SV characterisation of COLO829 (Top). SV types and size distributions and the circos plot shows SV distribution for chromosome 1. b Identification of a homozygous 12 kbp deletion affecting PTEN on chromosome 10 (Bottom). Source data are provided as a Source Data file.
Interestingly, our pstools method supports a homozygous 12 kbp deletion affecting PTEN on chromosome 10 (see Fig. 5). Our method can find SVs that occur due to a combination of multiple events on the same chromosome or different chromosomes producing breakage-fusion-bridge events; for example, chromosome 3 has fusion events from chromosomes 10, 12 and 615. We also found a known breakage-fusion-bridge event on chromosome 15 that has insertions from chromosomes 6 and 2015. We also assessed the copy number profile from our SV calls against the copy number profile from the raw HiFi and Hi-C sequencing data. Fig 6 shows the coverage distribution of the raw HiFi and HiC data and the coverage distribution of the phased sequences, where we observe strong correlations between the sequencing technologies and our phased sequences. Overall, our datasets and chromosome-scale haplotype reconstruction pstools method provide a useful resource and streamlined approach for analysing the full spectrum of structural variations in complex cancer genomes that can potentially facilitate downstream haplotype-aware analyses of long-range promoter-enhancer interactions in regulatory networks. Thus, it provides a simple method for clinicians to dissect the full spectrum of SVs for individual patients that should facilitate better diagnosis and disease management, including predicting therapeutic responses.
In our analysis, there were a few complex centromeric regions that were excluded from our phased sequences. The next potential step is to incorporate ultra-long (UL) nanopore sequencing data into the computational graphs (as already demonstrated for segmental duplication; ) to produce traversals for phased sequences in centromeres. Despite these limitations, our work enables the routine production of fully phased sequences at the chromosome scale that can be applied to hundreds/thousands of clinical and ethnically diverse samples for further biological discoveries.
After data collection, the raw sequencing subreads were imported into the SMRTLink 9.0 bioinformatics tool suite (Pacific Biosciences) for processing. Intramolecular error correcting was performed using the circular consensus sequencing (CCS) algorithm to produce highly accurate (>Q20) CCS reads, each requiring a minimum of 3 polymerase passes.
For local phasing and global phasing evaluation, we computed the switch and hamming error rates respectively. The switch error rate is the number of local switches divided by #heterozygous sites, while the hamming error rate is the hamming distance on the global level divided by #heterozygous sites. We implemented an efficient k-mer based method and used maximum Hi-C read support to detect switch errors on heterozygous positions. The process is to first find heterozygous k-mers (hets) from phased assemblies using 31-mers. After that, we map Hi-C reads to the assemblies using 31-mers. If there are >5 reads that support a switch between consecutive hets in assemblies, we consider a haplotype switch. In the hamming error calculations, we consider every switch support het pair in hamming distance for a global view of phasing errors (this implicitly penalises any long switches). We perform this operation for the whole scaffold/contigs over all scaffolds/contigs. This evaluation operation is made available in pstools subcommand phasing_error. Even Hi-C based pstools phasing evaluation has been experimented with and applied to diverse HPRC samples. ( ).
The author is thankful to George Church from Harvard Medical School, Anthony Schmitt from Arima Genomics and Nancy Francoeur for Mt. Sinai for providing necessary support on sequencing technologies. Thanks are also due to the Human Pangenome Reference consortium (HPRC) for HiFi and Hi-C datasets. Lastly, the author acknowledges the productive comments from anonymous reviewers and the support from the Novo Nordisk Foundation (NNF21OC0069089). 152ee80cbc
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