• 相关产品 +
  • 相关服务 +
  • 相关应用 +
  • 相关案例 +
  • 相关下载 +

咨询信息

我们的产品覆盖了化学信息学,生物信息学,以及实验室信息管理
针对您的研究需求

我们为您选择最适合您的产品
上传时间:2016-09-30 09:52:35
Version: v2.5
Release Date: 22-Jun-2015


New Features / Enhancements:

Strand NGS now has comprehensive support for methylation analysis. In addition to the already existing workflow for bisulfite sequencing, a new workflow for the analysis of MeDIP-Seq data has been introduced. This workflow includes calibration curve-based data normalization, CpG coverage analysis, methylation signal detection for regions of interest (ROIs) or whole genome, and detection of differentially  methylated regions (DMR) to produce hypo- and hyper-methylated regions / genes. Further, downstream functionalities such as GO and pathway analysis can also be performed on the entities of interest. 

The tool now comes with an efficient way to handle large-scale RNA-Seq projects where large number of samples arrive in batches over a long period. In such cases, separate experiments can be created for each batch of samples and quantification performed on them.  These experiments can later be combined for an overall analysis without the need to run the compute-intensive quantification step again. The quantification node is automatically created in the new experiment by combining raw counts from individual experiments and re-running normalization.

DNA-Seq workflow introduces a new step that detects structural variants (SVs) based on alignment of reads that span breakpoints. DNA alignment has been enhanced to align such reads by splitting them into two segments that map to non-adjacent regions on the genome. Detection of SVs using such split-aligned reads provides breakpoints with a higher precision compared to the already existing SV detection method based on paired reads, provided there is sufficient coverage at the breakpoints. The new method reports the detected events as a region list containing the breakpoint regions along with the inferred SVs. Each event can be visualized in the elastic genome browser by double-clicking on the corresponding row in the region list.

Further, the following auxiliary features have been introduced to support handling of split reads:
 

A new filter option 'Filter by Alignment Type' is provided in DNA-Seq analysis experiments to filter normal and different types of split reads. 

Genome Browser functionality has been enhanced to include support for displaying split reads. Split reads are shown with serrations at the split edge and are connected by dotted line. Also, 'Filter Options' functionality in the right click menu on the read track now includes split-read specific parameters to allow for filtering or retaining split reads. 

Default DNA-Seq alignment and analysis pipelines have been updated to include support for split alignment and SV detection from split reads.
 
Two additional QC plots are made available in the RNA-Seq experiments. The 'Gene-type' plot gives the number of reads aligning to different categories of genes. The 'Genic-region' plot gives the distribution of reads aligned to different genic regions such as exons, introns, exon-intron junctions, etc. Filters corresponding to these two QC plots are available in the 'Filters' section of the workflow.
 
Correlation analysis is now possible in multi-omics experiments (MOA) to compute Pearson correlation between entities or samples within or across experiments. This is applicable only to RNA-Seq and small RNA analysis experiments.

It is now possible to visualize sample meta-data along with the clustering tree. Both numerical and non-numerical data attributes such as drug dose / response, tumor staging, tumor size, treatment time, etc. can be visualized.  

A new option named 'Search in genome also even if alignment found against transcriptome' is added in Tools ->Options of RNA Alignment. This option is 'checked' by default and is applicable when RNA Alignment is run against both transcriptome and genome. Unchecking it will mimic the behavior of TopHat.

In the quantification step of RNA-Seq analysis, novel gene/exon discovery step is now turned OFF by default. If needed, it can be turned ON from Tools->Options.

In targeted CNV workflow, CNV results can now be visualized in genome browser to simultaneously show copy number and z-scores for all samples in the same track. Sample chooser button is provided on the top right corner of the track to select sample(s) to be highlighted. Selected samples are highlighted while others are shown in the background for easy comparison.   

Targeted CNV output can also be visualized in a web browser by right clicking on the CNV region list and selecting 'Visualize in Web Browser'. This will show the copy number (CN) values as a bar chart for one sample across whole genome. The z-score of the CN call is represented by the intensity of the bar color. Clicking on any chromosome will display the detailed view of the chromosome in the bottom pane along with additional annotations such as GC%, overlapping genes, and cytoband information. One can also switch to multi-sample view in which genome-wide data for all samples is shown as a heat map. Again clicking on any chromosome will launch a detailed view in the bottom pane. Multiple CNV outputs can be visualized in different browser windows enabling effective comparison.

In CNV analysis, coverage profiles can now be created using GC-corrected coverage data. It is also possible now to export, and import coverage profiles using the right click option on the Targets folder in the Annotations Manager.

A new 'Export VCF' functionality is provided in the Utilities section of the DNA-Seq workflow to export multiple types of variants (SNPs, MNPs, CNVs, or SVs) for each sample into a single VCF file.

In ChIP-Seq experiments, output of the peak detection step is structured differently now. A region list is created with the list of detected peaks, under which two child lists are created. The first child is the same region list as the parent but with gene annotations. If more than one gene is annotated to a single peak region, separate row is created for each gene in this region list. Second child is an entity list containing all the annotated genes.

Additional columns like p-value, FDR and Fold Enrichment are added to the output of MACS algorithm in ChIP-Seq workflow. 

A new tab has been added in the readlist inspector that shows the multi-sample information from the notes section in a tabular form. This allows easy access to information such as '%Filtered Reads', '%Duplicate Reads', '%Realigned Reads', etc.

Strand NGS can now correctly handle SAM / BAM files with array tags. This provides increased compatibility for BAM files generated by tools such as Ion Reporter.

The number of retained log folders has been increased from 10 to 100 for improved trouble-shooting.

A new R script is now available for differential expression analysis using the DESeq2 method.

Several new python scripts are now packaged with the software:
  
'SampleStats.py' takes read list, SNP node, and targeted region QC result as inputs and combines the sample-wise statistics from all of them into a text file with one row of information per sample.

'MergeSBV_MBV.py' is used to create a merged variant node in the navigator by merging SBVs and MBVs for each sample. This merged variant node is also used in the new 'Export VCF' functionality.

'FilterReads.py' provides a flexible set of filters for filtering reads based on alignment score, read quality, aligned sequence length, raw sequence length, etc.

'ScatterPlot.py' takes an entity list as an input and generates a scatter plot displaying normalised signal values for the selected two samples.  An option to label the data points with fold-change values or regulation (up, down) is also provided. 
   
Bug Fixes:


VAL importer would in some cases either drop insertions or add incorrect supporting base for insertions. This has been fixed.

SNP Detection with additional quality measures would fail for some non-human organisms. This has been fixed.

SNP filters defined on absolute number of supporting reads or total reads would give wrong results. This has been fixed.

Importing compressed files in fastq.gz format would fail if they are padded with trailing 0s. This has been fixed.

When multiple RNA samples are aligned, one of the intermediate files is deleted for the last sample before the novel discovery phase which might cause minor inaccuracies in the results for the last sample. This has been fixed.

Exported alignment reports would have wrong formatting for some columns. This has been fixed.

Sometimes 'Target Region QC' would fail in RNA-Seq experiments with an 'end less than start' exception. This has been fixed.

RNA alignment step in the pipelines would always run with the default 'Alignment Type' option overriding the specified option. This has been fixed.

SNP detection would fail if the base qualities are not available for the read data. This is now fixed by leaving the 'Average Base Quality' column blank in such cases.

Filtered SNPs would sometimes appear in the multi-sample report. This has been fixed.

In the computation of PV4 biases for the SNPs, the mapping qualities are capped at 60 though the values assigned by Strand NGS are in the range [0-254], resulting in wrong p-values for the mapping quality bias. This has been fixed. Also, the base quality bias is now computed after ignoring the bases with quality below the cut-off specified in  Tools → Options → DNA Variant Analysis → SNP Detection.
 
Targeted CNV detection would fail randomly in some situations due to multiple threads writing to the same 'Targeted QC' output. This has been fixed.
      
Local realignment would fail on samples which have hard clipped reads especially at the boundary of the realignment region. This has been fixed.

Known Issues:

There may be some inaccuracies in local realignment output due to ignoring the soft-clipped portion while sorting the reads.