總結(jié)（看的有限）：
1. 大部分方法為基于深度（劃分區(qū)間檢測(cè)）的方法（FPKM、區(qū)域堿基數(shù)的類似FPKM算法）。
2.?大部分軟件會(huì)使用對(duì)照樣本作為reference作為基線，但是reference本身的方差可能會(huì)比較大，方差可能是bias也可能是CNV等，方差的差異在算法處理過程中是不能消除的。并且大部分使用對(duì)照的算法在檢測(cè)common CNV的時(shí)候可能都不準(zhǔn)確，比如人群頻率50%的CNV，reference處理的時(shí)候，可能會(huì)把單拷貝作為正常二倍體處理，這樣正常二倍體可能會(huì)被作為三倍體檢出。
3. 外顯子數(shù)據(jù)矯正常利用GC含量、mappability對(duì)覆蓋深度進(jìn)行矯正。比如GC矯正，一般是對(duì)于一個(gè)窗口w，標(biāo)準(zhǔn)化后的深度，等于窗口原始深度值/具有相同GC含量窗口的深度值。
4. 檢測(cè)CNV之前一般會(huì)有質(zhì)控，去除一些bias較大的區(qū)間，比如考慮區(qū)間覆蓋度，樣本整體覆蓋情況，GC含量極端區(qū)間等。
5. 數(shù)據(jù)降噪方法常見 PCA、SVD（一般去除前k個(gè)noise）。一般應(yīng)用這類方法的時(shí)候，也就可能去除掉common CNV的信號(hào)，所以會(huì)看到有些軟件在檢測(cè)common的性能上不太好。commom CNV有考慮的，比如CLAMMS的一個(gè)主要優(yōu)化點(diǎn)就是同時(shí)考慮的common的CNV的特征，做批次效應(yīng)去除的時(shí)候不用深度文件，而是用picard產(chǎn)生的metrics。另外CODEX2，在無正常對(duì)照的時(shí)候「也需要一堆樣本同時(shí)檢測(cè)」可以檢測(cè)所有樣本的common CNV，文章數(shù)據(jù)表現(xiàn)很好。
6. CNV檢測(cè)算法常見HMM，CBS，新一點(diǎn)的方法還會(huì)用機(jī)器學(xué)習(xí)，其他的使用比較少也看不太懂~檢測(cè)區(qū)間一般是跨越多個(gè)外顯子，也有能做到單外顯子水平的，但是比較少且recall不太好（deletion相對(duì)更容易做到）~
題目：
1. CopyDetective: Detection threshold-aware copy number variant calling in whole-exome sequencing data.
2. Detection of copy-number variations from NGS data using read depth information: a diagnostic performance evaluation.
3. Copy Number Variation Detection Using Total Variation.
4. A highly sensitive and specific workflow for detecting rare copy-number variants from exome sequencing data.
5. Copy number variation profiling in pharmacogenes using panel-based exome resequencing and correlation to human liver expression.
6. A machine-learning approach for accurate detection of copy number variants from exome sequencing.
7. Atlas-CNV: a validated approach to call single-exon CNVs in the eMERGESeq gene panel.
8. CODEX2: full-spectrum copy number variation detection by high-throughput DNA sequencing.
9. Clinical analysis of germline copy number variation in DMD using a non-conjugate hierarchical Bayesian model.
10. Preprocessing Sequence Coverage Data for More Precise Detection of Copy Number Variations.
11. Integrative DNA copy number detection and genotyping from sequencing and array-based platforms.
12. WISExome: a within-sample comparison approach to detect copy number variations in whole exome sequencing data.
13. Anaconda: AN automated pipeline for somatic COpy Number variation Detection and Annotation from tumor exome sequencing data.
14. ExCNVSS: A Noise-Robust Method for Copy Number Variation Detection in Whole Exome Sequencing Data.
15. Accurate clinical detection of exon copy number variants in a targeted NGS panel using DECoN
16. Homozygous and hemizygous CNV detection from exome sequencing data in a Mendelian disease cohort.
17. CNVkit: Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing
18. CLAMMS: a scalable algorithm for calling common and rare copy number variants from exome sequencing data.
19. A Sparse Model Based Detection of Copy Number? Variations From Exome Sequencing Data.
20. DeAnnCNV: a tool for online detection and annotation of copy number variations from whole-exome sequencing data.
21. CopywriteR: DNA copy number detection from off-target sequence data.
22. Allele-specific copy-number discovery from whole-genome and whole-exome sequencing.
23. CODEX: a normalization and copy number variation detection method for whole exome sequencing.
24. Combinatorial approach to estimate copy number genotype using whole-exome sequencing data.
25. Assessing copy number from exome sequencing and exome array CGH based on CNV spectrum in a large clinical cohort.
26. Detection of internal exon deletion with exon Del.
27. cnvCapSeq: detecting copy number variation in long-range targeted resequencing data.
28. Inferring copy number and genotype in tumour exome data
29. cnvOffSeq: detecting intergenic copy number variation using off-target exome sequencing data.
30. Identification of copy number variants from exome sequence data.
31. PatternCNV: a versatile tool for detecting copy number changes from exome sequencing data.
32. EXCAVATOR: detecting copy number variants from whole-exome sequencing data
33. CoNVEX: copy number variation estimation in exome sequencing data using HMM.
34. Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation
35. Modeling read counts for CNV detection in exome sequencing data
36. Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth.
37. An exome sequencing pipeline for identifying and genotyping common CNVs associated with disease with application to psoriasis.
38. A robust model for read count data in exome sequencing experiments and implications for copy number variant calling.
39. Copy number variation detection and genotyping from exome sequence data
40. CONTRA: copy number analysis for targeted resequencing
41. cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate
42. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing
43. Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data
44. Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV.
45. CNV-seq, a new method to detect copy number variation using high-throughput sequencing
