Nanopore sequencing

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Table of contents

1.1 DNA REPLICATION MECHANISM AND THE CORRESPONDING KNOWLEDGE
1.1.1 CELL CYCLE
1.1.2 REPLICATION ORIGINS
1.1.3 REPLICATION UNIT
1.1.4 THE COMPLETE BIOLOGICAL REPLICATION INITIATION PROCESS
1.1.5 REPLICATION TIMING
1.2 REPLICATION REGULATION IN TIMING AND ORIGIN LOCATION
1.2.1 THE GENETIC AND EPIGENETIC MODIFICATIONS AROUND ORIGINS
1.2.2 STOCHASTIC MODEL OF INITIATION-TIMING REGULATION
1.3 THE CURRENT TECHNOLOGIES USED FOR ORIGIN IDENTIFICATION BY BULK DATA
1.3.1 SNS-SEQ
1.3.2 BUBBLE TRACK
1.3.3 MCM / ORC CHIP-SEQ
1.3.4 EDU-SEQ-HU
1.3.5 INI-SEQ
1.3.6 OK-SEQ
1.4 THE CURRENT SINGLE-MOLECULE TECHNOLOGIES USED FOR ORIGIN IDENTIFICATION
1.4.1 DNA COMBING
1.4.2 NANOPORE SEQUENCING
1.5 A NOVEL METHOD: ORM (OPTICAL REPLICATION MAPPING)
1.5.1 BIONANO HIGH-THROUGHPUT DNA FIBER MAPPING MATERIAL AND METHODS, AND BASIC ORM SIGNAL ANALYSES
2.1 CELL LINES
2.1.1 CELL SYNCHRONIZATION
2.1.2 CELL LABELING
2.2 OPTICAL REPLICATION MAPPING
2.3 DATA FORMAT OF BIONANO
2.3.1 BNX
2.3.2 RCMAP AND QCMAP
2.3.3 XMP
2.4 THE CALCULATION OF GENOMIC POSITIONS FOR THE RED SIGNALS
2.5 DATA INTEGRATION BY JAR PACKAGES AND OUTPUT FORMAT
2.5.1 ALLRAWDATAREFINING.JAR AND ITS OUTPUT FORMAT
2.5.2 GENERATEGTF_BYALLDATAREFINING_REFORMAT.JAR AND ITS OUTPUT FORMAT
2.6 HOT SPOTS FILTERING
2.6.1 HOT SPOTS
2.7 SEGMENTATION FOR ORM LABELING SIGNALS
2.8 THE RELIABILITY TEST FOR ORM SEGMENTATION
2.8.1 TRACK THE TRAJECTORY OF SEPARATED REPLICATION FORKS
2.8.2 THE UNEXPECTED LENGTH DISTRIBUTION IN ALL DATASETS
2.8.3 TWO HYPNOSIS FOR EXPLAINING THE UNEXPECTED LENGTH DISTRIBUTION
2.8.4 VERIFICATION OF POTENTIAL MODEL
2.8.5 REGAINING THE NEGLECTED SIGNALS
2.8.6 THE EXPLANATION FOR SPARSE LABELING REPLICATION INITIAL ZONE CALLING
3.1 CALCULATION OF NORMALIZED ORM SIGNAL DENSITY
3.2 NORMALIZED SIGNAL DENSITY SMOOTHING
3.3 PEAK AREA RECOGNITION
3.4 CORE REGION REFINING
3.4.1 THE AGGREGATED DENSITY PERCENTAGE
3.4.2 ESTIMATE PROPER SIGNAL PERCENTAGE CUTOFF TO CALL CORE REGIONS OF INITIATION ZONES
3.5 FILTERING AND INITIAL ZONE CALLING
3.5.1 OVERLAPPED REPLICATES NUMBER FILTERING
3.5.2 THE OTHER STANDARD TO ESTIMATE THE QUALITY OF CORE REGION
3.5.3 K-MEANS CLUSTERING FOR IZ LENGTH ADJUSTMENT FORK DIRECTIONALITY ANALYSIS
4.1 FDI: FORK DIRECTION INDEX
4.2 THE TRIALS FOR IDENTIFICATION OF FORK DIRECTION OF INDIVIDUAL TRACKS
4.2.1 THE MACHINE LEARNING CLASSIFIER
4.2.2 FAILED ATTEMPT TO INTRODUCE THE SECOND LABELING SIGNAL
4.3 GENOME-WIDE REPLICATION KINETICS IN ASYNCHRONOUS CELLS DEEPER DERIVATIVE DATA MINING FOR ORM IZS
5.1 STOCHASTIC MODEL
5.1.1 EARLY INITIATION EVENTS IN LATE-REPLICATING DOMAINS
5.1.2 LATE-REPLICATING SIGNALS ARE NOT NOISE DATA
5.1.3 FIRING EFFICIENCY IS CORRELATED WITH REPLICATION TIMING
5.1.4 NO SPECIFIC INITIATION SITES
5.1.5 COMPUTATIONAL SIMULATION CONFIRMS THE STOCHASTIC MODEL
5.2 COMPARISON BETWEEN REPLICATION ORIGINS MAPPED BY DIFFERENT APPROACHES
5.2.1 MUTUAL AUTHENTICATION
5.2.2 DIFFERENT FIRE EFFICIENCY AND REPLICATION TIMING COMPARISON
5.3 THE EPIGENETIC MODIFICATION MARKS AROUND INITIATION ZONES
5.3.1 THE EPIGENETIC MODIFICATION MARKS ENRICHED AT ORM INITIAL ZONES CONCLUSION AND PERSPECTIVES
6.1 MAIN CONCLUSION
6.1.1 ORM – A FUTURE TREND IN INITIATION DETECTION: SINGLE-MOLECULE, CHEAP AND HIGH-THROUGHPUT
6.1.2 DIRECT FIRE EFFICIENCY DETECTION REVEALS THAT INITIATIONS ARE NOT CLUSTERED
6.1.3 ORM DATA SUPPORT A STOCHASTIC MODEL IN REPLICATION TIMING REGULATION