scholarly journals Redesigning chromosomes for optimized Hi-C assay provides insights on loop formation and homologs pairing during meiosis

2017 ◽  
Author(s):  
Muller Héloïse ◽  
Scolari F. Vittore ◽  
Mercy Guillaume ◽  
Agier Nicolas ◽  
Aurèle Piazza ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Loop Formation ◽  
Diploid Strain ◽  
Entire Region ◽  
Signal To Noise ◽  
Chromosome Conformation ◽  
Homolog Pairing ◽  
Restriction Sites ◽  
Definition Of ◽  
Genomic Regions

AbstractIn all chromosome conformation capture based experiments the accuracy with which contacts are detected varies considerably because of the uneven distribution of restriction sites along genomes. In addition, repeated sequences as well as homologous, large identical regions remain invisible to the assay because of the ambiguities they introduce during the alignment of the sequencing reads along the genome. As a result, the investigation of homologs during meiosis prophase through 3C studies has been limited. Here, we redesigned and reassembled in yeast a 145kb region with regularly spaced restriction sites for various enzymes. Thanks to this Syn-3C design, we enhanced the signal to noise ratio and improved the visibility of the entire region. We also improved our understanding of Hi-C data and definition of resolution. The redesigned sequence is now distinguishable from its native homologous counterpart in an isogenic diploid strain. As a proof of principle, we track the establishment of homolog pairing during meiotic prophase in a synchronized population. This provides new insights on the individualization and pairing of homologs, as well as on their internal restructuration into arrays of loops during meiosis prophase. Overall, we show the interest of redesigned genomic regions to explore complex biological questions otherwise difficult to address.

scholarly journals The Multi-resolution CLEAN

1991 ◽  
Vol 131 ◽  
pp. 268-271 ◽  
Author(s):  
B.P. Wakker ◽  
U.J. Schwarz
Keyword(s):  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Extended Sources ◽  
Noise Ratio ◽  
Definition Of

AbstractWe describe a modification to CLEAN which alleviates some problems for extended sources. This is accomplished by combining the results of a number of conventional CLEAN operations, each done at a different resolution. The algorithm is called “Multi-Resolution Clean” or “MRC”. Experiments on model sources have shown that it works well even when the source is so extended that the usual CLEAN becomes impractical. For extended sources, MRC enhances the signal-to-noise ratio, resulting in an easier definition of the area of signal. Moreover, MRC is in principle faster than a standard CLEAN because less δ-functions are needed. This work was published in Astr. Ap., 200, 312.

SIMILARITY OF OUTPUT TRACES AS A SEISMIC OPERATOR CRITERION

Geophysics ◽  
1955 ◽  
Vol 20 (2) ◽  
pp. 254-269 ◽  
Author(s):  
Stephen M. Simpson
Keyword(s):  
Input Signal ◽  
Input Data ◽  
Signal To Noise Ratio ◽  
Linear Operators ◽  
High Input ◽  
Signal To Noise ◽  
Numerical Work ◽  
Input And Output ◽  
Definition Of

The problem of emphasizing signals on multiple trace seismograms is approached by considering a relationship between the input and output records. It is proposed that the transformation to output record be one which causes the output traces to be most “similar” or “in phase” according to a certain definition of this property. If the noise and signal are “properly behaved,” it may be demonstrated that a linear transformation chosen by this criterion must have a response emphasizing frequency ranges of high input signal‐to‐noise ratio. The determination of such a transformation from the input data alone is carried out for discrete linear operators. The numerical work involved in computing such operators is formidable. As an example the computations were carried out for a mixture of an artificial signal introduced into a noise record. The results are about as good as those obtained with conventional filtering techniques depending on prior knowledge of input signal‐to‐noise ratios.

scholarly journals Hi-C analyses with GENOVA: a case study with cohesin variants

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Robin H van der Weide ◽  
Teun van den Brand ◽  
Judith H I Haarhuis ◽  
Hans Teunissen ◽  
Benjamin D Rowland ◽  
...  
Keyword(s):  
R Package ◽  
Loop Formation ◽  
Chromosome Conformation ◽  
Genome Wide ◽  
Score Analysis ◽  
A Genome ◽  
Contact Frequency ◽  
Wide Scale ◽  
Genomic Regions ◽  
User Friendly

Abstract Conformation capture-approaches like Hi-C can elucidate chromosome structure at a genome-wide scale. Hi-C datasets are large and require specialised software. Here, we present GENOVA: a user-friendly software package to analyse and visualise chromosome conformation capture (3C) data. GENOVA is an R-package that includes the most common Hi-C analyses, such as compartment and insulation score analysis. It can create annotated heatmaps to visualise the contact frequency at a specific locus and aggregate Hi-C signal over user-specified genomic regions such as ChIP-seq data. Finally, our package supports output from the major mapping-pipelines. We demonstrate the capabilities of GENOVA by analysing Hi-C data from HAP1 cell lines in which the cohesin-subunits SA1 and SA2 were knocked out. We find that ΔSA1 cells gain intra-TAD interactions and increase compartmentalisation. ΔSA2 cells have longer loops and a less compartmentalised genome. These results suggest that cohesinSA1 forms longer loops, while cohesinSA2 plays a role in forming and maintaining intra-TAD interactions. Our data supports the model that the genome is provided structure in 3D by the counter-balancing of loop formation on one hand, and compartmentalization on the other hand. By differentially controlling loops, cohesinSA1 and cohesinSA2 therefore also affect nuclear compartmentalization. We show that GENOVA is an easy to use R-package, that allows researchers to explore Hi-C data in great detail.

Dose Limited Resolution

1998 ◽  
Vol 4 (S2) ◽  
pp. 802-803
Author(s):  
D. Van Dyck ◽  
A.J. den Dekker ◽  
J. Sijbers ◽  
E. Bettens
Keyword(s):  
Point Spread Function ◽  
Communication Channel ◽  
Unit Area ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Imaging Device ◽  
Point Spread ◽  
Spread Function ◽  
Minimal Contrast ◽  
Definition Of

1. IntroductionThe definition of resolution as introduced by Lord Rayleigh [1] is related to the width of the point spread function of the imaging device. In this definition, noise has not been taken into account. Another definition of resolution has been introduced by Rose [2] in the field of radar and TV. Here the resolution is defined in terms of the dose (D) (i.e. number of imaging particles per unit area) and the signal to noise ratio SNR (i.e. the minimal contrast) A third definition of resolution is based on the idea that the microscope is a communication channel between the object and the observer. The resolution can then be rephrased as the amount of information that is transmitted by the channel in the sense as defined by Shannon [3] as a number of bits per unit area. This definition however does not describe how this information can be deduced and what its precision is.

Sign in / Sign up

Export Citation Format

Share Document